Detailed Action
This communication is in response to the Application filed on 12/16/2024.
Claims 1-14, 17, 20-24 are pending and have been examined.
Claims 1-14, 17, 20-24 are rejected.
Apparent priority: 11/1/2022.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/17/2026, 12/18/2024 have been considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 17, 20-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, recites the limitation “the target audio signal” in limitation line 9. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation “the target audio signal” in limitation line 11. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-7 and 20-24 are rejected as being dependent on a rejected base claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14, 17, 20-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Independent Claim 1, Claim 1 recites,
“1. (Original) An audio signal encoding method, comprising: acquiring a high-frequency residual signal and a low-frequency residual signal of a target audio frame; [This relates to a human using auditory systems to acquire high and low frequency signals of an audio frame.]
suppressing a frequency component in the low-frequency residual signal within a target frequency range to acquire an encoded suppression signal, wherein a center frequency of the target frequency range is a fundamental-tone frequency of the low-frequency residual signal; [This relates to a mathematical process a human can perform using pen and paper.]
performing spectrum inversion on the encoded suppression signal to acquire a spectrum inversion signal; [This relates to a mathematical process a human can perform using pen and paper.]
acquiring a high-frequency energy gain of the target audio signal according to the spectrum inversion signal and the high-frequency residual signal; and [This relates to a mathematical process a human can perform using pen and paper.]
generating encoded data of the target audio frame according to the high-frequency energy gain. [This relates to a mathematical process a human can perform using pen and paper.]
The Dependent Claim does not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
Regarding Independent Claim 8, Claim 8 recites:
“8. (Original) An audio signal decoding method, comprising: parsing encoded data of a target audio frame to acquire low-frequency encoding information; [This relates to a mathematical process a human can perform using pen and paper.]
decoding the low-frequency encoding information to acquire a low-frequency signal and a low-frequency residual signal; [This relates to a mathematical process a human can perform using pen and paper.]
suppressing a frequency component in the low-frequency residual signal within a target frequency range to acquire a decoded suppression signal, wherein a center frequency of the target frequency range is a fundamental-tone frequency of the low-frequency residual signal; [This relates to a mathematical process a human can perform using pen and paper.]
performing spectrum inversion on the decoded suppression signal to acquire a low- frequency excitation signal; [This relates to a mathematical process a human can perform using pen and paper.]
performing signal reconstruction according to the low-frequency excitation signal to acquire a high-frequency signal; and [This relates to a mathematical process a human can perform using pen and paper.]
generating an audio signal of the target audio frame according to the low-frequency signal and the high-frequency signal. [This relates to a mathematical process a human can perform using pen and paper.]
The Dependent Claim does not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
Regarding Independent Claim 17, Claim 17 is a device claim with limitations similar to that of claim 8 and is rejected under the same rationale.
This judicial exception is not integrated into a practical application. In particular, claim 17 recites additional elements of “processor” and “memory”. For example, in [003311-00332] there is the description:
[003311 The processor may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate or a transistor logic device, a discrete hardware component, or the like. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
[00332 The memory may include a volatile memory, a random access memory (RAM) and/or a non-volatile memory and other forms in a computer-readable medium, such as a read only memory (ROM) or a flash random access memory (flash RAM). The memory is an example of the computer-readable medium.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a processor and memory is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claim 2 recites,
“2. (Original) The method according to claim 1, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal, comprises:
performing pre-emphasis processing on the low-frequency residual signal based on a high-pass filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the encoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 3 recites,
“3. (Original) The method according to claim 1, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal, comprises: performing filtering processing on the low-frequency residual signal based on a slope filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the encoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 4 recites,
“4. (Original) The method according to claim 1, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal comprises: performing notch processing on the frequency component within the target frequency range based on a second-order notch filter, to acquire an encoded notch signal; and [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
performing whitening processing on the encoded notch signal to acquire the encoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 5 recites,
“5. (Original) The method according to claim 1, wherein performing spectrum inversion on the encoded suppression signal to acquire the spectrum inversion signal comprises: modifying an amplitude of a sampling point with an odd index in the encoded suppression signal into its opposite value to acquire the spectrum inversion signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 6 recites,
“6. (Currently Amended) The method according to claim 1, wherein acquiring the high-frequency residual signal and the low-frequency residual signal of the target audio frame comprises: performing frequency division on the target audio frame to obtain a low-frequency signal and a high-frequency signal; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
performing linear prediction analysis on the high-frequency signal to acquire a first linear prediction coefficient (LPC); [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
converting the first linear prediction coefficient into a line spectrum pair (LSP) coefficient; restoring the line spectrum pair coefficient to a second linear prediction coefficient; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
evenly dividing the high-frequency signal into a preset number of sub-signals; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
separately performing filtering processing on each sub-signal based on the second linear prediction coefficient to acquire a residual signal of each sub-signal and acquire the high- frequency residual signal; and [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
encoding the low-frequency signal to acquire low-frequency encoding information and the low-frequency residual signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 7 recites,
“7. (Original) The method according to claim 6, wherein generating the encoded data of the target audio frame according to the high-frequency energy gain comprises: encoding the low-frequency encoding information, the line spectrum pair coefficient and the high-frequency energy gain to generate the encoded data of the target audio frame. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 9 recites,
“9. (Original) The method according to claim 8, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the decoded suppression signal comprises: performing pre-emphasis processing on the low-frequency residual signal based on a high-pass filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the decoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 10 recites,
“10. (Original) The method according to claim 8, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the decoded suppression signal comprises: performing filtering processing on the low-frequency residual signal based on a slope filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the decoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 11 recites,
“11. (Original) The method according to claim 8, wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range, to acquire the decoded suppression signal, comprises: performing notch processing on the frequency component within the target frequency range based on a second-order notch filter, to acquire a decoded notch signal; and [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
performing whitening processing on the decoded notch signal to acquire the decoded suppression signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 12 recites,
“12. (Original) The method according to claim 8, wherein inverting a spectrum value of a sampling point in the decoded suppression signal meeting a preset condition, to acquire the spectrum inversion signal comprises: modifying an amplitude of a sampling point with an odd index in the decoded suppression signal into its opposite value, to acquire the low-frequency excitation signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 13 recites,
“13. (Currently Amended) The method according to claim 8, wherein the encoded data of the target audio frame further comprises an LSP coefficient and a high- frequency energy gain; and [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
performing signal reconstruction according to the low-frequency excitation signal to acquire the high-frequency signal comprises: performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
Dependent claim 14 recites,
“14. (Original) The method according to claim 13, wherein performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal, comprises: acquiring an energy gain corresponding to each sub-signal in the high-frequency energy gain; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
acquiring a residual signal of each sub-signal according to the low-frequency excitation signal and the energy gain of each sub-signal; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
restoring the LSP coefficient to an LPC; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
acquiring each prediction sub-signal according to the LPC; [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
generating each sub-signal according to each prediction sub-signal and the residual signal of each sub-signal; and [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.]
generating the high-frequency signal according to each sub-signal. [This is a series of mathematical computations and steps that can be performed in the human mind or using pen and paper.] No additional limitations present.
As to dependent claim 20, claim 20 is a device claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to dependent claim 21, claim 21 is a device claim with limitations similar to that of claim 3 and is rejected under the same rationale.
As to dependent claim 22, claim 22 is a device claim with limitations similar to that of claim 4 and is rejected under the same rationale.
As to dependent claim 23, claim 23 is a device claim with limitations similar to that of claim 5 and is rejected under the same rationale.
As to dependent claim 24, claim 24 is a device claim with limitations similar to that of claim 6 and is rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 8-10, 17, 20, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Mauro (U.S. Patent Number US 6233549 B1) in view of Kim (U.S. Patent Number US 20140337021 A1) and further in view of Choo (U.S. Patent Number US 20070282599 A1).
Regarding independent Claim 1, Mauro teaches
1. (Original) An audio signal encoding method, comprising: acquiring a high-frequency residual signal and a low-frequency residual signal of a target audio frame; (see Mauro (4:45-60) “(18) A preferred embodiment determines an energy estimate for a low frequency channel and an energy estimate for a high frequency channel, so that N=2. The low frequency channel corresponds to frequency range from 250 to 2250 Hz, while the high frequency channel corresponds to frequency range from 2250 to 3500 Hz. The current channel energy of the low frequency channel may be determined by summing the energy of the FFT points corresponding to 250-2250 Hz, and the current channel energy of the high frequency channel may be determined by summing the energy of the FFT points corresponding to 2250-3500 Hz.”) suppressing a frequency component in the low-frequency residual signal within a target frequency range to acquire an encoded suppression signal, (see Mauro(3:10-15) “(6) The noise suppressor 108 suppresses noise in signal s(n). The spectral enhancer 109 amplifies the low frequency components of the speech signal in accordance with the present invention.”) wherein a center frequency of the target frequency range is a fundamental-tone frequency of the low-frequency residual signal; (see Mauro (3:20-40) “(8) As discussed more fully below, the inventive enhancer identifies a selected frequency component in a digitized signal and selectively boosts signals within a predetermined range thereof. In the illustrative embodiment, the digitized signal is a frequency domain transformed speech signal. The speaker unique fundamental frequency of the speech is computed using pitch delay information and is thus dynamic from frame to frame and also speaker to speaker. This defines the center point of a gain window which is applied to select frequency components. Only such fundamental frequency components which exhibit a large enough signal to noise ratio have the amplification function applied. Thus, this function can be applied in a speech processor 106 having a noise suppression system 108 which has knowledge of the signal quality in each frequency bin. The gain window is ramped up and hanged over to smooth the amplification function between successive frames.”)(see Mauro (5:60-65) “(23) …For example, the speech processor 106 may comprise a variable rate vocoder (not shown) which determines the encoding rate for each frame of input signal.”)
Mauro does not specifically teach performing spectrum inversion on the encoded suppression signal to acquire a spectrum inversion signal; However, Kim does teach this limitation (see Kim [0030] The input audio 104 may include target speech and/or interfering (e.g., undesired) sounds. For example, the target speech in the input audio 104 may include speech from one or more users. The interfering sounds in the input audio 104 may be referred to as noise. For example, noise may be any sound that interferes with or obscures the target speech (by masking the target speech, by reducing the intelligibility of the target speech, by overpowering the target speech, etc., for example). Different kinds of noise may occur in the input audio 104. For example, noise may be classified as stationary noise, non-stationary noise and/or music noise. Examples of stationary noise include white noise (e.g., noise with an approximately flat power spectral density over a spectral range and over a time period) and pink noise (e.g. noise with a power spectral density that is approximately inversely proportional to frequency over a frequency range and over a time period). Examples of non-stationary noise include interfering talkers and noises with significant variance in frequency and in time. Examples of music noise include instrumental music (e.g., sounds produced by musical instruments such as string instruments, percussion instruments, wind instruments, etc.).”)
Mauro and Kim are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mauro to incorporate performing spectrum inversion on the encoded suppression signal to acquire a spectrum inversion signal of Kim. This allows for improved signal quality as recognized by Kim [0005].
Mauro in view of Kim does not specifically teach acquiring a high-frequency energy gain of the target audio signal according to the spectrum inversion signal and the high-frequency residual signal; However, Choo does teach this limitation (see Choo[0036-0037] “In operation 135, an excitation spectrum is generated in the high frequency band of which frequency is higher than a predetermined frequency, by using the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130. That is, in operation 135, the excitation spectrum may be generated by patching either the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130 to the high frequency band or by folding the generated spectrum of the excitation signal or the extracted excitation spectrum over the high frequency band so that the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130 and the generated spectrum are symmetrical with respect to the predetermined frequency. [0037] In operation 140, the high frequency signal obtained in operation 100 is transformed from the time domain to the frequency domain so as to generate the high frequency spectrum. Examples of a mode in which the high frequency signal is transformed in operation 140 include FFT, MDCT, etc.”) and generating encoded data of the target audio frame according to the high-frequency energy gain. However, Choo does teach this limitation. (see Choo [0038] “In operation 150, a gain is calculated using the excitation spectrum generated in operation 135 and the high frequency spectrum generated in operation 140. The gain calculated in operation 150 is used when a decoder restores a high frequency spectrum by using the spectrum of a decoded excitation signal for a low frequency signal. In other words, when the decoder generates the high frequency spectrum by using the spectrum of the excitation signal for the low frequency signal, the gain is used to control the envelope of the high frequency spectrum.”)
Mauro in view of Kim and Choo are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Mauro and Kim to incorporate acquiring a high-frequency energy gain of the target audio signal according to the spectrum inversion signal and the high-frequency residual signal; and generating encoded data of the target audio frame according to the high-frequency energy gain of Choo. This allows for improved sound quality and data compression ratio in an audio encoding and decoding system as recognized by Choo [0045].
Regarding independent Claim 8, Mauro teaches
8. (Original) An audio signal decoding method, comprising: parsing encoded data of a target audio frame to acquire low-frequency encoding information; (see Mauro (5:3-10) “(20) The speech detector 208 also comprises a rate decision element 212, which selects the data rate of the input signal from a predetermined set of data rates. In certain communication systems, data is encoded so that the data rate may be varied from one frame to another. This is known as a variable rate communication system. The voice coder which encodes data based on a variable rate scheme is typically called a variable rate vocoder.”) decoding the low-frequency encoding information to acquire a low-frequency signal and a low-frequency residual signal; (see Mauro (6:15-26) “(25) where N refers to the numbers of samples of the speech frame, t.sub.1 and t.sub.2 refer to the boundaries within the T samples for which the NACF is evaluated. The NACF is evaluated based on the formant residual signal, e(n). Formant frequencies are the resonance frequencies of speech. A short-term filter is used to filter the speech signal to obtain the formant frequencies. The residual signal obtained after filtering by the short-term filter is the formant residual signal and contains the long-term speech information, such as the pitch, of the signal. The formant residual signal may be derived as explained later in this description.”) (see Mauro (1:1-10 “(3) This invention relates to telecommunications systems. Specifically, the present invention relates to a system and method for digitally encoding and decoding speech.”) suppressing a frequency component in the low-frequency residual signal within a target frequency range to acquire a decoded suppression signal, (see Mauro (9:14-21) “(49) The determination regarding the presence of speech is also provided to a noise suppression gain estimator 220a. The noise suppression gain estimator 220a determines the gain, and thus the level of noise suppression, for the frame of input signal. If the speech decision element 216 has determined that speech is not present, then the gain for the frame is set at a predetermined minimum gain level. Otherwise, the gain is determined as a function of frequency.”) wherein a center frequency of the target frequency range is a fundamental-tone frequency of the low-frequency residual signal; (see Mauro (10:10-20) “(54) In accordance with the present teachings and as mentioned above, the spectral enhancer 109 is provided as part of the speech processor 106 of FIG. 1. As shown in FIG. 2, the spectral enhancer 109 includes a pitch delay element 203, a speech fundamental frequency estimator 205, and a spectral enhancement gain estimator 220b. As discussed more fully below, the speech fundamental frequency estimator 205 divides the speech sampling rate by the pitch delay and thereby ascertains a fundamental frequency of the speech.”)
Mauro does not specifically teach performing signal reconstruction according to the low-frequency excitation signal to acquire a high-frequency signal; However, Kim does teach this limitation (see Kim [0030] The input audio 104 may include target speech and/or interfering (e.g., undesired) sounds. For example, the target speech in the input audio 104 may include speech from one or more users. The interfering sounds in the input audio 104 may be referred to as noise. For example, noise may be any sound that interferes with or obscures the target speech (by masking the target speech, by reducing the intelligibility of the target speech, by overpowering the target speech, etc., for example). Different kinds of noise may occur in the input audio 104. For example, noise may be classified as stationary noise, non-stationary noise and/or music noise. Examples of stationary noise include white noise (e.g., noise with an approximately flat power spectral density over a spectral range and over a time period) and pink noise (e.g. noise with a power spectral density that is approximately inversely proportional to frequency over a frequency range and over a time period). Examples of non-stationary noise include interfering talkers and noises with significant variance in frequency and in time. Examples of music noise include instrumental music (e.g., sounds produced by musical instruments such as string instruments, percussion instruments, wind instruments, etc.).”)
Mauro and Kim are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mauro to incorporate performing signal reconstruction according to the low-frequency excitation signal to acquire a high-frequency signal of Kim. This allows for improved signal quality as recognized by Kim [0005].
Mauro in view of Kim does not specifically teach performing spectrum inversion on the decoded suppression signal to acquire a low-frequency excitation signal; However, Choo does teach this limitation (see Choo[0036-0037] “In operation 135, an excitation spectrum is generated in the high frequency band of which frequency is higher than a predetermined frequency, by using the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130. That is, in operation 135, the excitation spectrum may be generated by patching either the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130 to the high frequency band or by folding the generated spectrum of the excitation signal or the extracted excitation spectrum over the high frequency band so that the spectrum of the excitation signal generated in operation 125 or the excitation spectrum extracted in operation 130 and the generated spectrum are symmetrical with respect to the predetermined frequency. [0037] In operation 140, the high frequency signal obtained in operation 100 is transformed from the time domain to the frequency domain so as to generate the high frequency spectrum. Examples of a mode in which the high frequency signal is transformed in operation 140 include FFT, MDCT, etc.”) and generating an audio signal of the target audio frame according to the low-frequency signal and the high-frequency signal. (see Choo [0038] “In operation 150, a gain is calculated using the excitation spectrum generated in operation 135 and the high frequency spectrum generated in operation 140. The gain calculated in operation 150 is used when a decoder restores a high frequency spectrum by using the spectrum of a decoded excitation signal for a low frequency signal. In other words, when the decoder generates the high frequency spectrum by using the spectrum of the excitation signal for the low frequency signal, the gain is used to control the envelope of the high frequency spectrum.”)
Mauro in view of Kim and Choo are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Mauro and Kim to incorporate performing spectrum inversion on the decoded suppression signal to acquire a low- frequency excitation signal; and generating an audio signal of the target audio frame according to the low-frequency signal and the high-frequency signal of Choo. This allows for improved sound quality and data compression ratio in an audio encoding and decoding system as recognized by Choo [0045].
Regarding Independent Claim 17, Claim 17 is a device claim with limitations similar to that of claim 8 and is rejected under the same rationale. Furthermore, Kim teaches 17. (Currently Amended) An electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program; and the processor is configured to, when executing the computer program, cause the electronic device to See Kim [0124] “The electronic device 1002 also includes memory 1061 in electronic communication with the processor 1043. That is, the processor 1043 can read information from and/or write information to the memory 1061. The memory 1061 may be any electronic component capable of storing electronic information. The memory 1061 may be random access memory (RAM), read-only memory (ROM), magnetic disk storage media, optical storage media, flash memory devices in RAM, on-board memory included with the processor, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), registers, and so forth, including combinations thereof.”)
Mauro and Kim are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Mauro to incorporate electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program; and the processor is configured to, when executing the computer program of Kim. This allows for improved signal quality as recognized by Kim [0005].
As to independent Claim 2 Mauro in view of Kim and further in view of Choo teach 2. (Original) The method according to claim 1,
Furthermore, Mauro teaches wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal, comprises: performing pre-emphasis processing on the low-frequency residual signal based on a high-pass filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the encoded suppression signal. (See Mauro (4:13-30) “(13) As shown in FIG. 2, the input audio signal s(n) is received by a preprocessor 202. The preprocessor 202 prepares the input signal for noise suppression and enhancement by performing pre-emphasis and frame generation. Pre-emphasis redistributes the power spectral density of the speech signal by emphasizing the high frequency speech components of the signal. Essentially performing a high pass filtering function, pre-emphasis emphasizes the important speech components to enhance the SNR of these components in the frequency domain. The preprocessor 202 may also generate frames from the samples of the input signal. In a preferred embodiment, 10 ms frames of 80 samples/frame are generated. The frames may have overlapped samples for better processing accuracy. The frames may be generated by windowing and zero padding of the samples of the input signal.”)(see Mauro (83) A block diagram of the synthesis circuit 400 of the present invention is shown in FIG. 10. Because switches 415 and 425(a and b) have two positions each, there are four possible modes in which the synthesis circuit 400 can operate. Excitation signal 420 can either come from direct excitation storage unit 405, or be generated from a codebook excitation generation unit 410, depending on the position of switch 415. If the excitation 420 was LTP encoded in the analysis stage, then coupled switches 425a and 425b direct the excitation signal to the inverse LTP encoding unit 435 for decoding, and then to the pitch shifter/envelope generator 460. Otherwise switches 425a and 425b direct the excitation signal 420 past the inverse LTP encoding unit 435,”)(see Mauro (1:5-10) “(3)…relates to a system and method for digitally encoding and decoding speech.”)
As to independent Claim 3 Mauro in view of Kim and further in view of Choo teach 3. (Original) The method according to claim 1,
Furthermore, Mauro teaches wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal, comprises: performing filtering processing on the low-frequency residual signal based on a slope filter to suppress the frequency component in the low-frequency residual signal within the target frequency range, to acquire the encoded suppression signal. (See Mauro (6:15:26) “(25) where N refers to the numbers of samples of the speech frame, t.sub.1 and t.sub.2 refer to the boundaries within the T samples for which the NACF is evaluated. The NACF is evaluated based on the formant residual signal, e(n). Formant frequencies are the resonance frequencies of speech. A short-term filter is used to filter the speech signal to obtain the formant frequencies. The residual signal obtained after filtering by the short-term filter is the formant residual signal and contains the long-term speech information, such as the pitch, of the signal. The formant residual signal may be derived as explained later in this description.”) (See Mauro Figure 2, Element 208, Element 220B.)(see Mauro, (13:20-30) “(82)… The use of the rampup (examiner interprets slope as “rampup”) count allows for a gradual increase in gain over successive frames for stable operation in cases where the signal-to-noise ratio is close to the threshold. Likewise, the use of a hangover count prohibits the enhancement function from turning on and off over successive frames in which the signal to noise ratio is close to the threshold.”)
As to dependent claim 9, claim 9 is a method claim with limitations similar to that of claim 2 and is rejected under the same rationale. (Examiner notes Mauro teaches decoding as previously established (see Mauro (1:5-10) “(3)…relates to a system and method for digitally encoding and decoding speech.”)
As to dependent claim 10, claim 10 is a method claim with limitations similar to that of claim 3 and is rejected under the same rationale. (Examiner notes Mauro teaches decoding as previously established) (see Mauro (1:5-10) “(3)…relates to a system and method for digitally encoding and decoding speech.”)
As to dependent claim 20, claim 20 is a device claim with limitations similar to that of claim 2 and is rejected under the same rationale.
As to dependent claim 21, claim 21 is a device claim with limitations similar to that of claim 3 and is rejected under the same rationale.
Claims 4, 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Mauro (U.S. Patent Number US 6233549 B1) in view of Kim (U.S. Patent Number US 20140337021 A1) and further in view of Choo (U.S. Patent Number US 20070282599 A1) and further in view of Yang (U.S. Patent Number US 20110295598 A1).
As to Claim 4, Mauro in view of Kim and further in view of Choo teaches 4. (Original) The method according to claim 1,
Mauro in view of Kim and further in view of Choo do not specifically teach wherein suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal comprises: performing notch processing on the frequency component within the target frequency range based on a second-order notch filter, to acquire an encoded notch signal; However, Yang does teach this limitation. (see Yang [0122] “Filter bank FB220 also includes an implementation PSH20 of highband synthesis processing path PSH12 that is configured to interpolate a highband signal SDH10 having a sampling rate of f.sub.SH (e.g., 7 kHz) by a non-integer factor f.sub.SW/f.sub.SH. Path PSH20 includes an implementation IH20 of interpolator IH10 that is configured to interpolate signal SDH10 by a factor of two to a sampling rate of f.sub.SH.times.2 (e.g., to 14 kHz), a spectral reversal block which may be implemented as described above with reference to module RHS10 of path PSH12, an interpolation block IH30 configured to interpolate the spectrally reversed signal by a factor of two to a sampling rate of f.sub.SH.times.4 (e.g., to 28 kHz), and a resampling block configured to resample the interpolated signal to a sampling rate of f.sub.SW (e.g., by a factor of 4/7). In this particular example, path PSH20 also includes an optional spectral shaping filter FSW10, which may be implemented as a lowpass filter configured to shape the signal to obtain a desired overall filter response and/or as a notch filter configured to attenuate a component of the signal at 7100 Hz. In a particular example, shaping filter FSW10 is implemented as a notch filter having the transfer function “) and performing whitening processing on the encoded notch signal to acquire the encoded suppression signal. (See Yang [0125] Filter bank FB220 also includes an implementation PSW20 of wideband synthesis processing path PSW12 that is configured to receive a wideband signal SDW10 having a sampling rate of f.sub.SW (e.g., 16 kHz) and to perform an interpolation by two to produce a wideband output signal SOW10 having a sampling rate of f.sub.s (e.g., 32 kHz). In this example, path PSW20 includes an implementation IW20 of interpolator IW10 (e.g., an FIR polyphase implementation as described herein) and an optional shaping filter (e.g., a second-order pole-zero filter).”) (See Yang [0131] FIG. 12B shows an example of a basic source-filter arrangement as applied to coding of the spectral envelope of narrowband signal SIL10. An analysis module calculates a set of parameters that characterize a filter corresponding to the speech sound over a period of time (typically ten or twenty milliseconds). A whitening filter (also called an analysis or prediction error filter) configured according to those filter parameters removes the spectral envelope to spectrally flatten the signal. The resulting whitened signal (also called a residual) has less energy and thus less variance and is easier to encode than the original speech signal. Errors resulting from coding of the residual signal may also be spread more evenly over the spectrum. The filter parameters and residual are typically quantized for efficient transmission over the channel. At the decoder, a synthesis filter configured according to the filter parameters is excited by a signal based on the residual to produce a synthesized version of the original speech sound. The synthesis filter is typically configured to have a transfer function that is the inverse of the transfer function of the whitening filter.”)
Mauro in view of Kim in view of Choo and Yang are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and of Choo to include suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal comprises: performing notch processing on the frequency component within the target frequency range based on a second-order notch filter, to acquire an encoded notch signal; and performing whitening processing on the encoded notch signal to acquire the encoded suppression signal of Yang. This allows for the use of processing paths having a smooth rolloff over the overlapped region, easier design, less computationally complex, and introduce less delay as recognized by Yang [0116].
As to dependent Claim 11 claim 11 is a method claim with limitation similar to that of claim 4 and is rejected under the same rationale. (examiner notes Yang teaches decoding in Figure 3.)
As to dependent claim 22, claim 22 is a device claim with limitations similar to that of claim 4 and is rejected under the same rationale.
Claims 5, 12 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Mauro (U.S. Patent Number US 6233549 B1) in view of Kim (U.S. Patent Number US 20140337021 A1) and further in view of Choo (U.S. Patent Number US 20070282599 A1) and further in view of Massie (U.S. Patent Number US 5698807 A).
As to Claim 5, Mauro in view of Kim and further in view of Choo teaches 5. (Original) The method according to claim 1,
Mauro in view of Kim and further in view of Choo do not specifically teach wherein performing spectrum inversion on the encoded suppression signal to acquire the spectrum inversion signal comprises: modifying an amplitude of a sampling point with an odd index in the encoded suppression signal into its opposite value to acquire the spectrum inversion signal. However, Massie does teach this limitation (see Massie (14:25-37) “(93) The excitation signal 440 is then shifted in pitch by the pitch shifter/envelope generator 460. The excitation signal 440 is pitch shifted by either slowing down or speeding up the playback rate, and this is accomplished in a sampled digital system by interpolations between the sampled points stored in memory. The preferred method of pitch shifting is described in the above-identified cross-referenced patent applications, which are incorporated herein by reference. This method will now be described.”) (see Massie (14:45-55) “(96) where C.sub.i (f) represents the i.sup.th coefficient which is a function of f. Note that the above equation represents an odd-ordered interpolator of order n, and is easily modifed to provide an even-ordered interpolator. The coefficients C.sub.i (f) represent the impulse response of a filter, which can be optimally chosen according to the specification of the above-identified cross-referenced patent applications, and is approximately a windowed sinc function.”) (see Massie (5:40-57) “(17) Excitation component 86 of input signal 51 is then extracted at inverse filtering stage 84. Inverse filtering stage 84 utilizes the filter parameter estimates 78 to generate the inverse filter 84. The excitations 86 are optionally subjected to long term predictive (LTP) analysis at LTP analysis stage 88. The LTP stage 88 requires pitch information 87 extracted from the input signal 51 by pitch analyzer 85. The LTP analysis requires single notes rather than chords or group averages as the input signal 51. During the initial portion of the analysis, process switch 98 directs the excitation signals to the codebook stage 96 for generation of a codebook. Once the codebook 96 has been generated, the excitation signal 90 is directed by switch 98 to the excitation encoder 92 for encoding as a string of codebook entries. These stages of the analysis circuit 50 are described in more detail below.”) (see Massie (4:55-5:5) “(13) Direct measurement of the formant is the most obvious method of formant spectrum determination. When the instrument to be analyzed has an obvious physical formant producing resonant structure, such as the body of a violin or guitar, this technique can be readily applied. The impulse response of the resonant structure may be determined by applying an audio impulse or white noise through a loudspeaker and recording the audio response by means of a microphone. The response is then digitized, and its Fourier transform gives the spectrum of the formants. This spectrum is then approximated to provide a formant filter by a filter parameter estimation technique. Filter parameter estimation techniques known in the art include the equation-error method, the Hankel norm, linear predictive coding, and Prony's method.”)
Mauro in view of Kim in view of Choo and Massie are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and of Choo to include suppressing the frequency component in the low-frequency residual signal within the target frequency range to acquire the encoded suppression signal comprises: performing notch processing on the frequency component within the target frequency range based on a second-order notch filter, to acquire an encoded notch signal; and performing whitening processing on the encoded notch signal to acquire the encoded suppression signal of Massie This allows for improved expressivity and coding efficiency as recognized by Massie (11:4-7).
As to dependent claim 12, claim 12 is a method claim with limitations similar to that of claim 5 and is rejected under the same rationale. (Examiner interprets excitation signal as inverse filtering within the reference citation (5:40-57) (17)).
As to dependent claim 23, claim 23 is a device claim with limitations similar to that of claim 5 and is rejected under the same rationale.
Claims 6, 7, 13, 14, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Mauro (U.S. Patent Number US 6233549 B1) in view of Kim (U.S. Patent Number US 20140337021 A1) and further in view of Choo (U.S. Patent Number US 20070282599 A1) and further in view of Jeong (U.S. Patent Number US 20140303967 A1).
As to Claim 6, Mauro in view of Kim and further in view of Choo teaches 6. (Currently Amended) The method according to claim 1,
Mauro in view of Kim and further in view of Choo do not specifically teach wherein acquiring the high-frequency residual signal and the low-frequency residual signal of the target audio frame comprises: performing frequency division on the target audio frame to obtain a low-frequency signal and a high-frequency signal; However, Jeong does teach this limitation (see Jeong [0043] “The pre-processing unit 109 may perform a pre-processing operation on the voice signal having the changed internal sampling frequency by the sampling converting unit 106. By the pre-processing, it is possible to effectively extract a voice parameter. For example, the pre-processing unit 109 may use the high-pass filtering or the pre-emphasis filtering to extract a frequency component of an important band. For example, the pre-processing unit 109 may focus an important band required for extracting a parameter by setting a cutoff frequency to be different depending on the bandwidth of a voice signal. The pre-processing unit 109 may perform a high-pass filtering to filter very low frequencies which are frequency bands including relatively less important information. For example, the pre-processing unit 109 boosts a high frequency band of an input voice signal and scales energy of a low frequency band and a high frequency band. By the boosting and the scaling, a resolution for linear prediction and analysis may be raised. [0047] The band dividing unit 112 may convert the sampling rate of an input super-wideband signal and may divide the frequency band thereof into an upper band and a lower band. For example, a voice signal of 32 kHz may be converted into a sampling frequency of 25.6 kHz. The voice signal converted into a sampling frequency of 25.6 kHz may be divided into an upper band and a lower band by 12.8 kHz. The lower band may be transmitted to the pre-processing unit 109 for filtering.”) performing linear prediction analysis on the high-frequency signal to acquire a first linear prediction coefficient (LPC); (see Jeong [0044] “The linear-prediction analysis unit 118 may calculate linear prediction coefficients (LPC). The linear-prediction analysis unit 118 may model a formant representing the entire shape of a frequency spectrum of a voice signal. The linear-prediction analysis unit 118 may calculate the LPC values so that the mean square error (MSE) of error values which are differences between an original voice signal and a predicted voice signal generated using the linear prediction coefficients calculated by the linear-prediction analysis unit 118. Various LPC coefficient calculating methods such as an autocorrelation method and a covariance method may be used to calculate the LPCs.”) converting the first linear prediction coefficient into a line spectrum pair (LSP) coefficient; restoring the line spectrum pair coefficient to a second linear prediction coefficient; (see Jeong [0045] “The linear-prediction quantization unit 124 may convert the LPCs extracted from the lower-band voice signal into transform coefficients of the frequency domain such as LSP or LSF and may quantize the transform coefficients. The LPCs have a wide dynamic range. Accordingly, when the LPCs are transmitted without any change, the compression rate is lowered. As a result, it is possible to generate LPC information with a small amount of information using transform coefficients transformed to the frequency domain. The linear-prediction quantization unit 124 may quantize and encode the LPC coefficient. The linear-prediction quantization unit 124 may transmit linear prediction residual signal. The linear prediction residual signal includes pitch information which are a signal from which formant components are excluded using the LPCs dequantized and transformed to the time domain, and a random signal. The linear prediction residual signal may be transmitted to the subsequent stage of the linear-prediction quantization unit 124. In the upper band, the linear prediction residual signal may be transmitted to the compensation gain predicting unit 157. In the lower band, the linear prediction residual signal in the lower band may be transmitted to the TCX mode executing unit 127 and the CELP mode executing unit 136.”) evenly dividing the high-frequency signal into a preset number of sub-signals; (see Jeong [0041] “The encoding operation of the voice encoder may vary depending on the bandwidth of a voice signal. For example, when an input voice signal is a super-wideband signal, the input voice signal is input to only the band dividing unit 112 and the sampling converting unit 106 is not activated. When an input voice signal is a narrowband signal or a wideband signal, the input voice signal is input to only the sampling converting unit 106 and the band dividing unit 112 and the constituent units 115, 121, 157, and 154 subsequent thereto are not activated. In some embodiments, the bandwidth checking unit 103 may not include in the voice encoder when the bandwidth of an input voice signal is fixed. “) separately performing filtering processing on each sub-signal based on the second linear prediction coefficient to acquire a residual signal of each sub-signal and acquire the high- frequency residual signal; and encoding the low-frequency signal to acquire low-frequency encoding information and the low-frequency residual signal. (see Jeong [0043] “The pre-processing unit 109 may perform a pre-processing operation on the voice signal having the changed internal sampling frequency by the sampling converting unit 106. By the pre-processing, it is possible to effectively extract a voice parameter. For example, the pre-processing unit 109 may use the high-pass filtering or the pre-emphasis filtering to extract a frequency component of an important band. For example, the pre-processing unit 109 may focus an important band required for extracting a parameter by setting a cutoff frequency to be different depending on the bandwidth of a voice signal. The pre-processing unit 109 may perform a high-pass filtering to filter very low frequencies which are frequency bands including relatively less important information. For example, the pre-processing unit 109 boosts a high frequency band of an input voice signal and scales energy of a low frequency band and a high frequency band. By the boosting and the scaling, a resolution for linear prediction and analysis may be raised. [0047] The band dividing unit 112 may convert the sampling rate of an input super-wideband signal and may divide the frequency band thereof into an upper band and a lower band. For example, a voice signal of 32 kHz may be converted into a sampling frequency of 25.6 kHz. The voice signal converted into a sampling frequency of 25.6 kHz may be divided into an upper band and a lower band by 12.8 kHz. The lower band may be transmitted to the pre-processing unit 109 for filtering.”)
Mauro in view of Kim and further in view of Choo and Jeong are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and Choo to include acquiring the high-frequency residual signal and the low-frequency residual signal of the target audio frame comprises: performing frequency division on the target audio frame to obtain a low-frequency signal and a high-frequency signal; performing linear prediction analysis on the high-frequency signal to acquire a first linear prediction coefficient (LPC); converting the first linear prediction coefficient into a line spectrum pair (LSP) coefficient; restoring the line spectrum pair coefficient to a second linear prediction coefficient; evenly dividing the high-frequency signal into a preset number of sub-signals; separately performing filtering processing on each sub-signal based on the second linear prediction coefficient to acquire a residual signal of each sub-signal and acquire the high- frequency residual signal; and encoding the low-frequency signal to acquire low-frequency encoding information and the low-frequency residual signal of Jeong. This allows for improved voice encoding and decoding efficiency as recognized by Jeong [0005-0006].
As to Claim 7, Mauro in view of Kim and further in view of Choo and further in view of Jeong teaches 7. (Original) The method according to claim 6,
Furthermore Jeong teaches wherein generating the encoded data of the target audio frame according to the high-frequency energy gain comprises: encoding the low-frequency encoding information, the line spectrum pair coefficient and the high-frequency energy gain to generate the encoded data of the target audio frame. (see Jeong [0045] “The linear-prediction quantization unit 124 may convert the LPCs extracted from the lower-band voice signal into transform coefficients of the frequency domain such as LSP or LSF and may quantize the transform coefficients.”) (see Jeong [0061] “The CELP synthesization unit 350 may calculate a synthesized voice signal on the basis of the reconstructed voice signal and the quantized linear prediction coefficients by performing the inverse processes of the linear prediction on the reconstructed excitation signal which is the inversely-transformed linear prediction residual signal quantized in the CELP mode. The voice signal reconstructed in the CELP mode may be supplied to the mode selecting unit 151 and may be compared with the voice signal reconstructed in the TCX mode.”) (see Jeong [0062] “The mode selecting unit 151 may compare the TCX-reconstructed voice signal generated from the excitation signal reconstructed in the TCX mode with the CELP-reconstructed voice signal generated from the excitation signal reconstructed in the CELP mode, may select the signal more similar to the original voice signal, and may encode mode information on the encoding mode. The selection information may be transmitted to the band predicting unit 154.”)
Mauro in view of Kim and further in view of Choo in view of Jeong are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and Choo and Jeong to include generating the encoded data of the target audio frame according to the high-frequency energy gain comprises: encoding the low-frequency encoding information, the line spectrum pair coefficient and the high-frequency energy gain to generate the encoded data of the target audio frame of Jeong This allows This allows for improved voice encoding and decoding efficiency as recognized by Jeong [0005-0006].
As to Claim 13, Mauro in view of Kim and further in view of Choo teaches 13. (Currently Amended) The method according to claim 8,
Mauro in view of Kim and further in view of Choo do not specifically teach wherein the encoded data of the target audio frame further comprises an LSP coefficient and a high- frequency energy gain; However, Jeong does teach this limitation (see Jeong [0045] “The linear-prediction quantization unit 124 may convert the LPCs extracted from the lower-band voice signal into transform coefficients of the frequency domain such as LSP or LSF and may quantize the transform coefficients. The LPCs have a wide dynamic range. Accordingly, when the LPCs are transmitted without any change, the compression rate is lowered. As a result, it is possible to generate LPC information with a small amount of information using transform coefficients transformed to the frequency domain. The linear-prediction quantization unit 124 may quantize and encode the LPC coefficient. The linear-prediction quantization unit 124 may transmit linear prediction residual signal. The linear prediction residual signal includes pitch information which are a signal from which formant components are excluded using the LPCs dequantized and transformed to the time domain, and a random signal. The linear prediction residual signal may be transmitted to the subsequent stage of the linear-prediction quantization unit 124. In the upper band, the linear prediction residual signal may be transmitted to the compensation gain predicting unit 157. In the lower band, the linear prediction residual signal in the lower band may be transmitted to the TCX mode executing unit 127 and the CELP mode executing unit 136.”) and performing signal reconstruction according to the low-frequency excitation signal to acquire the high-frequency signal comprises: performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal. (see Jeong [0051-0052] “The TCX inverse transform unit 220 may inversely transform the linear prediction residual signal, which has been transformed to the frequency domain by the transform unit, to an excitation signal of the time domain on the basis of the quantized information. [0052] The TCX synthesization unit 230 may calculate a synthesized voice signal using the inversely-transformed linear prediction coefficient values quantized in the TCX mode and the reconstructed excitation signal.”)
Mauro in view of Kim and further in view of Choo and Jeong are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and Choo to include the encoded data of the target audio frame further comprises an LSP coefficient and a high- frequency energy gain; and performing signal reconstruction according to the low-frequency excitation signal to acquire the high-frequency signal comprises: performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal of Jeong. This allows for improved voice encoding and decoding efficiency as recognized by Jeong [0005-0006].
As to Claim 14, Mauro in view of Kim and further in view of Choo and further in view of Jeong teaches 14. (Original) The method according to claim 13,
Furthermore, Jeong teaches wherein performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal, comprises: acquiring an energy gain corresponding to each sub-signal in the high-frequency energy gain; (see Jeong [0091] “Here, four low-frequency bands (X.sub.n(k) n=0, - - - , 3 may be fixed and four important frequency bands out of 32 high-frequency bands may be selected and defined as quantization-selected bands based on an energy distribution. Finally, the quantization-selected bands may be 8 bands ({tilde over (X)}.sub.n(k) n=0, - - - , 7) including four low-frequency bands and four high-frequency bands. As described above, the number of frequency bands to be quantized is arbitrary and may be changed. Information on the positions of the selected bands may be transmitted to the voice decoder.”) acquiring a residual signal of each sub-signal according to the low-frequency excitation signal and the energy gain of each sub-signal; (see Jeong [0093] “Referring to FIG. 8, the horizontal axis in the upper part of FIG. 8 represents the frequency band (800) when an original linear prediction residual signal is transformed to the frequency domain. As described above, the frequency transform coefficients of the linear prediction residual signal may be divided into 32 bands depending on the frequency bands, and 8 frequency bands of four fixed low-frequency bands 820 and four selected high-frequency bands 840 in the frequency bands of the original linear prediction residual signal may be selected frequency bands to be quantized. In selecting 8 selected frequency bands, 32 frequency bands other than the four fixed low-frequency bands are arranged in a descending order of energy and 8 upper frequency bands are selected. restoring the LSP coefficient to an LPC; acquiring each prediction sub-signal according to the LPC; (see Jeong [0080] “Aw(z) represents a filter including quantized linear prediction coefficients (LPCs) subjected to LPC analysis and quantization. The input signal may pass through the Aw(z) filter to output a linear prediction residual signal. The linear prediction residual signal may be a target signal to be encoded in the TCX mode.”) generating each sub-signal according to each prediction sub-signal and the residual signal of each sub-signal; and (see Jeong [0087] “In the method of dividing a voice signal band according to the embodiment of the present invention, the linear prediction residual signal may be divided into a low frequency band and a high frequency band depending on the frequencies and may be encoded. By using the method of dividing a band, it is possible to determine whether to perform quantization depending on the degree of important of the band. The following embodiment of the present invention will describe a method of quantizing some fixed low frequency bands and selectively quantizing bands having a large energy portion out of upper high frequency bands. A band to be quantized may be referred to as a frequency band to be quantized, plural fixed low frequency bands may be referred to as fixed low-frequency bands, and plural high-frequency bands to be selectively quantized may be referred to as selected high-frequency bands.”) generating the high-frequency signal according to each sub-signal. (see Jeong [0152] The reconstructed candidate linear prediction residual signals generated by combination of the signals of the low-frequency bands and the signals of the candidate-selected high-frequency bands passing through the IFFT and the band-selection inverse DFT may pass through the filter 1/Aw(z) which is a synthesis filter present in the AbS block to generate audible signals. These signals pass through an auditory weighting filter to generate reconstructed voice signals. The signal-to-noise ratio of these signals pass through an auditory weighting filter can be calculated based on the voice signals acquired by causing the linear prediction residual signals not subjected to the quantization which are target signals of the TCX mode. This process may be repeatedly performed by the number of candidate combinations to finally determine the combination of candidate bands having the highest signal-to-noise ratio as the selected bands. The quantized transform coefficient values of the finally-selected high-frequency bands are selected from the quantized transform coefficient values of the candidate-selected high-frequency bands quantized in step S1520.”)
Mauro in view of Kim and further in view of Choo in view of Jeong are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method combination of Mauro and of Kim and Choo and Jeong to include wherein performing signal reconstruction according to the low-frequency excitation signal, the LSP coefficient and the high-frequency energy gain, to acquire the high-frequency signal, comprises: acquiring an energy gain corresponding to each sub-signal in the high-frequency energy gain; acquiring a residual signal of each sub-signal according to the low-frequency excitation signal and the energy gain of each sub-signal; restoring the LSP coefficient to an LPC; acquiring each prediction sub-signal according to the LPC; generating each sub-signal according to each prediction sub-signal and the residual signal of each sub-signal; and generating the high-frequency signal according to each sub-signal of Jeong This allows This allows for improved voice encoding and decoding efficiency as recognized by Jeong [0005-0006].
As to dependent claim 24, claim 24 is a device claim with limitations similar to that of claim 6 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
GAMPP (US Publication No. US 20200020347 A1) APPARATUS AND METHODS FOR PROCESSING AN AUDIO SIGNAL
Abstract:
An apparatus for processing an audio signal includes a separator for separating a first portion of a spectrum of the audio signal from a second portion of the spectrum of the audio signal, the first portion having a first signal characteristic and the second portion having a second signal characteristic. The apparatus includes a first bandwidth extender for extending a bandwidth of the first portion using first parameters associated with the first signal characteristic, for obtaining a first extended portion and includes a second bandwidth extender for extending a bandwidth of the second portion using second parameters associated with the second signal characteristic, for obtaining a second extended portion. The apparatus includes a combiner configured for using the first extended portion and the second extended portion for obtaining an extended combined audio signal.
Visser (US Publication No. US 20070021958 A1) Robust Separation Of Speech Signals In A Noisy Environment
Abstract:
A method for improving the quality of a speech signal extracted from a noisy acoustic environment is provided. In one approach, a signal separation process is associated with a voice activity detector. The voice activity detector is a two-channel detector, which enables a particularly robust and accurate detection of voice activity. When speech is detected, the voice activity detector generates a control signal. The control signal is used to activate, adjust, or control signal separation processes or post-processing operations to improve the quality of the resulting speech signal. In another approach, a signal separation process is provided as a learning stage and an output stage. The learning stage aggressively adjusts to current acoustic conditions, and passes coefficients to the output stage. The output stage adapts more slowly, and generates a speech-content signal and a noise dominant signal. When the learning stage becomes unstable, only the learning stage is reset, allowing the output stage to continue outputting a high quality speech signal.
SUZUKI (US Patent No. 20190035413) AUDIO ENCODING APPARATUS AND AUDIO ENCODING METHOD
Abstract:
There is provided an audio encoding apparatus including a memory, and a processor coupled to the memory and the processor configured to determine whether a tone is included in a boundary between a low-frequency that is a frequency bandwidth below a predetermined frequency of an input signal and a high-frequency that is a frequency bandwidth above the predetermined frequency of the input signal, suppress a tone in one of the low-frequency and the high-frequency, encode the input signal having the low-frequency to generate a low-frequency code, encode the input signal having the high-frequency to generate a high-frequency code, and generate an encoded stream by multiplexing the low-frequency code and the high-frequency code.
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/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/EDGAR X GUERRA-ERAZO/Primary Examiner, Art Unit 2656
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