DETAILED ACTION
This office action is in response to Applicant’s Request for Continued Examination (RCE), received on 03/17/2026. Claims 1, 13, and 18 have been amended. Claim 23 has been added. Claims 1-2, 4-15, 17-20, 22-23 are pending and have been considered.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/17/2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119
(a)-(d). The certified copies have been filed for the parent Application No. CN-2021080314, filed on 03/11/2021 and parent Applicant No. EP21174289.5, filed on 05/18/2021.
Information Disclosure Statement
The information disclosure statement(s) submitted on 09/11/2025 and 02/17/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pgs. 7-10, filed 03/17/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 103 (Lyren in view of Jensen, further in view of Derkx) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Falk et al. (“A Non-Intrusive Quality and Intelligibility Measure of Reverberant and Dereverberated Speech”), hereinafter Falk. Falk discloses “A modulation spectral representation is investigated for non-intrusive quality and intelligibility measurement of reverberant and dereverberated speech. The representation is obtained by means of an auditory-inspired filterbank analysis of criticalband temporal envelopes of the speech signal. Modulation spectral insights are used to develop an adaptive measure termed speech to reverberation modulation energy ratio” (abstract). Falk is being incorporated in response to Applicant’s amendment and/or Applicant’s arguments against Derkx. See updated rejections below.
Applicant's arguments filed 03/17/2026, see pgs. 11-12, with respect to the rejection of independent claim 13 have been fully considered but they are not persuasive.
Applicant’s representative asserts, “
A. ‘separating the input audio signal into two or more spatial components’
In rejecting independent claim 13, the Office asserted that Lyren discloses ‘separating the input audio signal into two or more spatial components,’ citing paragraph [0154] of Lyren. In particular, the Office asserted that ‘one stereo recording is presented to the user as two segments, one segment being the left stereo channel and one segment being the right stereo channel.’ (Office Action, page 9).
Even assuming arguendo that the left stereo channel and the right stereo channel correspond to ‘two or more spatial components’ (which Applicant does not concede), nowhere does Lyren disclose or suggest ‘separating [an] input audio signal’ to obtain the left stereo channel and the right stereo channel. Instead, in Lyren, the audio signal is stored as two segments, a left stereo channel and a right stereo channel, and no separation is performed.
Accordingly, Lyren does not disclose or suggest ‘separating the input audio signal into two or more spatial components,’ and instead merely describes obtaining a left stereo channel and a right stereo channel.”
In response, the examiner respectfully disagrees with Applicant’s assertions that Lyren does not disclose or suggest separating audio input into two or more spatial components. [0092] of Lyren discloses “[0092] The audio input is separated into two or more audio segments, channels, or tracks”. This is taken in view of the previously cited presentation of stereo audio as two segments.
Applicant’s representative continues, “
B. ‘classifving each of the two or more spatial components as one of the at least two media types’
The Office asserted that Lyren discloses ‘classifying each of the two or more spatial components as one of at least two media types,’ citing paragraph [0151] of Lyren. In particular, the Office asserted that this portion of Lyren states ‘a track and/or segment can be defined as speech or voice, music, speech with music, noise, gaming sounds, animal sounds, machine generated sounds, or other types of sound.’ (Office Action, page 9).
As discussed above, the Office asserted that the left stereo channel and the right stereo channel of Lyren correspond to ‘the two or more spatial components’ recited in claim 13. Nowhere does Lyren disclose or suggest classifying the left stereo channel and the right stereo channel as one of at least two media types. Instead, Lyren merely states that a track or segment may be of some type. In other words, Lyren does not disclose or suggest classification of each spatial component as one of at least two media types.”
In response, the examiner respectfully disagrees with Applicant’s assertion that Lyren does not disclose or suggest classifying the left stereo channel and the right stereo channel as one of at least two media types. Specifically, Lyren discloses classification of tracks and/or segments as previously cited with [0151]. Taking this in view of the previously referenced [0092] of Lyren which discloses separation of input audio into channels, tracks, or segments, the examiner asserts that classifications of a track and/or segment of audio in a situation with multiple tracks and/or segments will result in multiple classifications, wherein Lyren also discloses spatial components such as left and right stereo channels. [0092] of Lyren appears to treat tracks, channels, and segments as synonymous terms; therefore, classification of tracks, wherein the tracks are left/right stereo channels, maps to the claim language as currently claimed.
Applicant’s representative continues, “
C. ‘combining classifications of each of the two or more spatial components’
The Office asserted that Lyren discloses ‘classifying the media type of the input audio signal by combining classifications of each of the two or more spatial components,’ citing paragraph [0149] of Lyren. (Office Action, page 10).
Paragraph [0149] of Lyren states ‘[t]he information includes ... a classification or type or source of the audio (e.g., a telephone call, a radio transmission, a television show, a game, a movie, audio output from a software application, etc.).’
As discussed above, the Office asserted that the left stereo channel and the right stereo channel described in paragraph [0154] of Lyren corresponds to ‘the two or more spatial components’ recited in claim 13. Nowhere does Lyren disclose or suggest ‘combining classifications’ of the left stereo channel and the right stereo channel. Accordingly, Lyren does not disclose or suggest ‘combining classifications of each of the two or more spatial components,’ let alone performing such combination to ‘classify the media type of the input audio signal.’ Rather, the cited portion of Lyren states that there may be information about a type or source of the audio, but the cited portions of Lyren have nothing to do with spatial components, classifying spatial components, or combining classifications of spatial components.”
In response, the examiner would like to refer to the generated outputs for input signals of Lyren as disclosed in Figs. 10A-B. Specifically, the tables of these figures describe audio information (in view of the audio information of the previously mapped [0149]). The examiner asserts that the channel/segment classifications of Lyren would be used to generate the “Sound Type” information for unheard audio, wherein a transfer function or impulse response is then selected based on the sound types: “[0217] The information stored in the tables and other information discussed herein can assist a user, an electronic device, and/or a computer program in making informed decisions on how to process sound (e.g., where to localize the sound, what transfer functions or impulse responses to provide to convolve the sounds…)”. Selecting transfer functions or impulse responses to be provided based on sound type indicates the transfer function or impulse response selected to be a classification of media type based on the spatial component classifications, wherein multiple channels will have multiple sound types. Further, [0279] of Lyren discloses “The segment ID allows the SLP selector to look up the input source of the segment in a table that lists the segments known by the system and the source to which the segment belongs. In this example embodiment, the segment ID is a required argument, and both the sound type and sound ID are optional arguments. If a sound type is not passed to the SLP selector then a determination is made of the sound type or probable sound type based on the sound ID (if known), sound source, analysis of the segment or other data”. As analyses of segments each contain individual classifications as previously disclosed, this indicates the entry in the table to be a combined classification, i.e. sound type, based on each segment/spatial component, i.e. left/right stereo channels.
Applicant’s arguments, see pg. 12, filed 03/17/2026, with respect to the rejection(s) of claim(s) 13 under 35 U.S.C. 102(a)(1) (with respect to the upmixing) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kim et al. (US-20200221242-A1), hereinafter Kim. Kim discloses “a converter configured to convert of frequency of an input stereo audio signal; a primary component analyzer configured to perform primary component analysis based on a signal from the converter; a feature extractor configured to extract a feature of a primary component signal based on a signal from the primary component analyzer; an envelope adjustor configured to perform envelope adjustment based on prediction performed on the basis of a deep neural network model; and an inverse converter configured to inversely convert a signal from the envelope adjustor to output an upmix audio signal of multi-channel” (abstract). See updated rejection below.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the entered abstract format does not comply with what is required. The entered abstract appears to be a copy of the first two pages of the international application from which this application claims priority (WO-20220192580-A1). Applicant is welcome to use the abstract from the prior application, but the format with which this is presented must be corrected. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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.
Claim(s) 1, 2, 4, 5, 8-10, 12, 20, 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of in view of Falk et al. (“A Non-Intrusive Quality and Intelligibility Measure of Reverberant and Dereverberated Speech”), hereinafter Falk, further in view of Jensen (US-20110255702-A1), hereinafter Jensen.
Regarding claim 1, Lyren discloses: a method for reverberation suppression ([0039] minimize reverberation), comprising:
receiving an input audio signal ([0044] system 100 obtains, receives, or retrieves audio input 150); and,
classifying a media type of the input audio signal as one of a group comprising at least: 1) speech; 2) music; or 3) speech over music ([0072] receives the audio input 240 and classifies this audio input as being either non-speech audio or speech audio, [0151] a track and/or segment can be defined as speech or voice, music, speech with music [Wherein segments/tracks represent portions/channels of audio and could equal one (see monophonic audio [0149]), indicating the larger input audio is classified according to the track/segment definitions]).
Lyren does not disclose:
calculating a two-dimensional acoustic-modulation frequency spectrum of the input audio signal,
wherein the two-dimensional acoustic-modulation frequency spectrum of the input audio signal represents energy as a function of acoustic frequency of the input audio signal and a modulation frequency of the input audio signal;
Falk discloses:
calculating a two-dimensional acoustic modulation frequency spectrum of the input audio signal ([Fig. 3a-3e], [The examiner asserts that generating a modulation spectrogram for reverberant speech (3b) requires receiving input audio to generate a spectrogram in view of the input audio of Lyren]),
wherein the two-dimensional acoustic-modulation frequency spectrum of the input audio signal represents energy as a function of acoustic frequency of the input audio signal and a modulation frequency of the input audio signal ([pg. 1770, Par. 2] Plots in Fig. 6(a) and (b) illustrate a representative example where the percentage of modulation energy present per acoustic frequency channel is plotted versus acoustic frequency, [As can be seen with the previously cited figures, each spectrogram plots acoustic frequency against modulation frequency, wherein the quantities represented by the shading of the figures is energy (as related to the percentages of Figs. 6)]).
Lyren and Falk are considered analogous art within speech recognition of noisy audio (as measured through quality/intelligibility of Falk, see [pg. 1766, par. 3]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren to incorporate the teachings of Falk, because of the novel way to generate a modulation spectrogram of input audio for purposes of classification (between clean speech, noisy/reverberant speech, etc.), improving the estimation of multiple dimensions of perceived audio coloration (used for classification) based on expected frequency ranges for clean and reverberant speech ([pg. 1768, par. 1]) which improves the quality measurement and intelligibility estimation of reverberant and dereverberated speech as would be applied to the audio classifications of Lyren ([Falk, Abstract]).
Lyren in view of Falk does not disclose:
determining whether to perform dereverberation on the input audio signal based at least on a determination that the media type of the input audio signal has been classified as speech; and,
in response to determining that dereverberation is to be performed on the input audio signal, generating an output audio signal by performing dereverberation on the input audio signal.
Jensen discloses:
calculating a two-dimensional acoustic-modulation frequency spectrum of the input audio signal ([0062] converting the input signal from a time domain representation to a time-frequency domain representation and providing as an output time-frequency signals [A time-frequency domain representation indicates a two-dimensional, i.e. time and frequency, representation which would show the modulation of the frequency of the received acoustic input audio as a function of time]).
Lyren, Falk, and Jensen are considered analogous art within speech recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk to incorporate the teachings of Jensen, because of the novel way to measure and monitor the amount of reverberation in an audio signal to be removed, improving the quality of speech recognition of the associated audio (Jensen, [0025]).
Falk further discloses:
determining a degree of reverberation of the input audio signal by calculating at least one of:
1) a ratio of energy in a high modulation frequency portion of the two-dimensional acoustic- modulation frequency spectrum to energy over all modulation frequencies in the two-dimensional acoustic-modulation frequency spectrum ([The examiner would like to note that, due to the disjunctive construction of the claim elements, this does not require a mapping]); or
2) a ratio of energy in the high modulation frequency portion of the two-dimensional acoustic-modulation frequency spectrum to energy in a low- modulation frequency portion of the two-dimensional acoustic-modulation frequency spectrum ([pg. 1769-1770, “C. Proposed Measure”] an adaptive measure termed speech to reverberation modulation energy ratio (SRMR) is proposed for non-intrusive quality diagnosis of (de)reverberant speech, [As can be seen by the equation, the ratio is comprised of modulation spectral energies (represented by E) for a lower and higher range of frequencies bands (frequency bands are represented by k)]).
Jensen further discloses:
wherein the high modulation frequency portion corresponds to a region of the two-dimensional acoustic-modulation frequency spectrum with modulation frequencies greater than a first predetermined threshold ([0033] In an embodiment, voice detection comprises an algorithm based on the determination of a measure of the modulation of the signal (e.g. a modulation index), where a voice signal is assumed to be present, if the modulation measure is above a predefined threshold value, [Wherein sections of audio with a voice signal present, i.e. containing an original speech and reverberations, will be high modulation as compared to sections without speaking, i.e. a silent portion signal and/or one with exclusively reverberations. Taken in view of the modulation spectrogram of Falk, which measures a quantity of energy over modulation frequencies, indicating it is reasonable to assert the modulation measure value of Jensen to be determined according to the energies on the modulation spectrogram of Falk]), and
wherein the low modulation frequency portion of the two-dimensional acoustic-modulation frequency spectrum corresponds to a region of the two-dimensional acoustic- modulation frequency spectrum with modulation frequencies less than a second predetermined threshold ([In view of the above excerpt disclosing a threshold for determining voice activity, reasonably understood to be high modulation as compared to a voice-less signal, it is apparent that anything below the predefined threshold will be low modulation as compared to the voice signals above the threshold. A similar rationale applied with regard to the combination of Falk in view of Jensen above can be extended here]);
determining whether to perform dereverberation on the input audio signal based at least on a determination that the media type of the input audio signal has been classified as speech and based on the degree of reverberation ([0064] In an embodiment, the presence of a human voice in the input microphone signal(s), as detected by a voice detector VD, activates the dereverberation algorithm, whereas the algorithm is deactivated when no voice is detected [Detecting voice before dereverberation indicates a classification that the audio contains speech, further in view of the previously disclosed modulation threshold for determining when speech signals are present indicating that the presence of human voice is detected based on the degree of reverberation as determined through the threshold of Jensen]); and,
in response to determining that dereverberation is to be performed on the input audio signal, generating an output audio signal by performing dereverberation on the input audio signal ([0062] dereverberation units (or algorithms) may be integrated receiving inputs from a number of microphones and delivering a directional output signal cleaned for reverberation effects [Cleaning a signal for reverberation effects indicates the signals is dereverberated, wherein received input from a microphone tracks to an audio input as would be received in Lyren]).
Regarding claim 2, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Jensen further discloses:
wherein the degree of reverberation is based on a reverberation time (RT60) ([0069] directionality algorithm is influenced by the reverberation time input REV (e.g. T60)), a Direct-to-Reverberant Ratio (DRR), an estimation of diffuseness, or any combination thereof ([The examiner would like to note that due to the disjunctive nature of the claim element, not all elements require a mapping]).
Regarding claim 4, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 2.
Jensen further discloses:
wherein determining whether to perform dereverberation on the input audio signal is based on a determination that the degree of reverberation exceeds a threshold ([0033] In an embodiment, the voice detection algorithm is adapted to modify the threshold value of the modulation measure depending on the value of the reverberation measure, e.g. so that the threshold value of the modulation measure is decreased when the reverberation measure (e.g. the reverberation time, e.g. the value of T60) is increased. [0069] The measure of reverberation time REV can e.g. be used to influence the degree of directionality implemented by the directionality algorithm [A time-based measure of reverberation, wherein that reverberation is used to determine a modulation measure, i.e. modulation can be reasonably assumed to be a form of dereverberation, indicates the modulation threshold to perform dereverberation is based on the degree/threshold of reverberation (and associated modulation), indicating the threshold to be based on the degree of reverberation]).
Regarding claim 5, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren further discloses:
wherein classifying the media type of the input audio signal comprises separating the input audio signal into two or more spatial components ([0154] in an example embodiment, one stereo recording is presented to the user as two segments, one segment being the left stereo channel and one segment being the right stereo channel, [In view of the previously disclosed classification of Lyren as applied to tracks/segments, i.e. components]);
optionally wherein the input audio signal is separated into the two or more spatial components in response to determining that the input audio signal comprises stereo audio ([0044] By way of example, the audio input includes, but is not limited to, monaural sound, stereo sound [In view of the above element which separates stereo sound into two channels, the disclosure to receive mono in addition to stereo indicates a determination of the received signal type to know whether division is necessary as would be for stereo audio]).
Regarding claim 8, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 5.
Lyren further discloses:
wherein classifying the media type of the input audio signal comprises classifying each of the two or more spatial components as one of: 1) speech; 2) music; or 3) speech over music ([0151] a track and/or segment can be defined as speech or voice, music, speech with music [Wherein a track/segment represents a portion of a larger audio signal, in view of the previously disclosed spatial segmentation for classification of Lyren (see [0154]), indicating classification of spatial components]); and,
wherein the media type of the input audio signal is classified by combining classifications of each of the two or more spatial components ([Fig. 7, 700], [0149] The information includes, but is not limited to, one or more of a file format of the audio, a classification or type or source of the audio (e.g., a telephone call, a radio transmission, a television show, a game, a movie, audio output from a software application, etc.), monophonic, stereo, or binaural… a subject matter of the content of the audio, an identify of voices or sounds or speakers in the audio, music in the audio input, noise in the audio input [Classification of the source of the audio based on content, tracking to a media type, wherein individual tracks within the audio file receive classifications, indicating the classification of the audio type/source is based on the individual track level classifications, i.e. if the tracks consist of music, dialogue, and background noise, the classification media type would be “movie”]).
Regarding claim 9, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren further discloses:
wherein classifying the media type of the input audio signal comprises separating the input audio signal into a vocal component and a non-vocal component ([0048] divides the audio input into speech segments and non-speech segments, [0151] a track and/or segment can be defined as speech or voice, music, speech with music, noise, gaming sounds, animal sounds, machine generated sounds, or other types of sound [Defining tracks, wherein the tracks comprise the input audio, to be specific to voice and other categories indicates a separation of vocal and non-vocal components in the input audio]); and,
optionally wherein the input audio signal is separated into the vocal component and the non-vocal component in response to determining that the input audio signal comprises a single audio channel ([0031] mono sound can be convolved with a person's HRIRs or HRTFs to generate binaural sound that is individualized for the person, [0036] divide or segment an audio input into different sounds or sound segments, such as dividing the audio input into different speech segments (e.g., one segment for each speaker or each voice) and different non-speech segments (e.g., one segment for music, one segment for background noise, etc.) [In response to determining the incoming audio is mono, binaural sound is generated, wherein Lyren discloses voice and non-voice classifications indicating the channels of the binaural sound could be vocal and non-vocal]).
Regarding claim 10, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 9.
Lyren further discloses:
wherein the classifying the media type of the input audio signal comprises:
classifying the vocal component as one of: 1) speech ([0151] a track and/or segment can be defined as speech or voice); or 2) non-speech ([0151] a track and/or segment can be defined as… animal sounds [Animal sounds tracks to a non-speech classification]);
classifying the non-vocal component as one of: 1) music ([0151] a track and/or segment can be defined as… music); or 2) non-music ([0151] a track and/or segment can be defined as… machine generated sounds [Defining machine generated sounds as distinct from music indicates they are non-music sounds]);
wherein the media type of the input audio signal is classified by combining the classification of the vocal component and the classification of the non-vocal component ([Fig. 7, 700], [0149] The information includes, but is not limited to, one or more of a file format of the audio, a classification or type or source of the audio (e.g., a telephone call, a radio transmission, a television show, a game, a movie, audio output from a software application, etc.)…a subject matter of the content of the audio, an identify of voices or sounds or speakers in the audio, music in the audio input, noise in the audio input, [Classification of the subject matter of the audio, tracking to a media type, wherein individual tracks within the audio file receive classifications, indicating the classification of the audio type/source is based on the individual track level classifications, i.e. if the vocal tracks consist of dialogue and dialogue over music and the non-vocal tracks consists of background noise and music, the classification media type could be assumed to be “movie”]).
Regarding claim 12, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren further discloses:
receiving a third input audio signal ([0044] system 100 obtains, receives, or retrieves audio input 150 [Referring to this input audio as a “third” signal does not affect the definition, i.e. applying the method of Lyren to a different, third signal to not be dereverberated would not change the functionality of Lyren in view of Jensen as Jensen discloses activation/deactivation of dereverberation (see below)]); and,
wherein the classification of the media type of the third input audio signal is one of: 1) music ([0072] classifies this audio input as being either non-speech audio or speech audio. Non-speech audio includes, but is not limited to, music); or 2) speech over music ([0093] audio segmenting process that segments audio input into two or more acoustic classes or audio events, such as music, clean speech, speech with noise, speech with music).
Jensen further discloses:
determining that dereverberation is not to be performed on the third input audio signal ([0064] In an embodiment, the presence of a user's own voice in the input microphone signal(s), as detected by an own voice detector OVD, deactivates the dereverberation algorithm [Disabling a dereveberation algorithm based on an identity within the audio signal indicates a determination not to perform reverberation]);
in response to determining that dereverberation is not to be performed on the third input audio signal, inhibiting a dereverberation algorithm from being performed on the third input audio signal ([0064] the presence of a human voice in the input microphone signal(s), as detected by a voice detector VD, activates the dereverberation algorithm, whereas the algorithm is deactivated when no voice is detected [Deactivation of a dereverberation algorithm indicates an inhibition of that algorithm’s performance, wherein the input microphone signal of Jensen could represent a third input audio as defined using Lyren without a change in functionality to Jensen]); and,
optionally wherein determining that dereverberation is not to be performed on the third input audio signal is based at least in part on: (a) a classification of a media type of the third input audio signal ([0064] the presence of a human voice in the input microphone signal(s), as detected by a voice detector VD, activates the dereverberation algorithm, whereas the algorithm is deactivated when no voice is detected [Classification of the media type as “speech”, i.e. presence of human voices, in order to determine dereverberation is not to be performed]) or (b) a determination that a degree of reverberation in the third input audio signal is below a threshold ([The examiner would like to note that due to the disjunctive nature of this claim, this element does not require a mapping. Further, the degree of reverberation threshold determination as mapped in claim 4 could be applied to this third input signal without a change in functionality]).
Regarding claim 20, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren further discloses:
an apparatus or system configured for implementing the method of claim 1 ([0023] Example embodiments are apparatus [See rejections of claim 1 for how the rejection would be applied to the apparatus of this claim]).
Regarding claim 22, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren further discloses:
one or more non-transitory media having software stored thereon, the software including instructions for controlling one or more devices to perform the method of claim 1 ([0325] In some example embodiments, the methods illustrated herein and data and instructions associated therewith, are stored in respective storage devices that are implemented as computer-readable and/or machine-readable storage media, physical or tangible media, and/or non-transitory storage media [See rejections of claim 1 for how the rejection would be applied to the non-transitory media of this claim]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Falk, further in view of Jensen, further in view of Kim et al. (US-20200221242-A1), hereinafter Kim, further in view of Rasmussen et al. (US-20170004846-A1), hereinafter Rasmussen.
Regarding claim 6, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 5.
Lyren in view of Falk, further in view of Jensen does not disclose:
wherein the two or more spatial components comprise a center channel and a side channel; and,
optionally wherein the method further comprises:
calculating a power of the side channel; and,
classifying the side channel in response to determining that the power of the side channel exceeds a threshold.
Kim discloses:
wherein the two or more spatial components comprise a center channel and a side channel ([0329] one side channel, [0330] channels such as the front, center, woofer, rear, and upstream channels); and,
optionally wherein the method further comprises:
calculating a power of the side channel ([0326] the ambient component signal may represent a component having a low correlation between two channels, such as a sound reflected by various paths or a reverberation sound, [0358] The second feature extractor 1045 may extract a feature such as… a power of the ambient component signal [An ambient component representing reverberation between two channels indicates at least one channel could be a side channel]).
Lyren, Falk, and Jensen, and Kim are considered analogous art within speech analysis/recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk, further in view of Jensen to incorporate the teachings of Kim, because of the novel way to upmix downmix stereo audio to a multichannel audio signal, improving/reducing spatial distortion (Kim, Abstract).
Lyren in view of Falk, further in view of Jensen, further in view of Kim does not disclose:
classifying the side channel in response to determining that the power of the side channel exceeds a threshold.
Rasmussen discloses:
classifying the side channel in response to determining that the power of the side channel exceeds a threshold ([0043] The threshold controller 63 preferably comprises a mapper 69 that maps each predefined audio signal class into an individual frequency-dependent level threshold T.sub.f,k, which may further depend on one or more signal parameters, such as e.g. frequency, bandwidth, level, power and/or energy, indicated in the one or more individual evaluation signals [In view of the previously defined side channel of Kim, indicating the mapping operation could be applied to the side channel, i.e. signal, without a change in functionality. Mapping audio into classes based on a threshold which is dependent upon power indicates the classification is power based, as it relates to thresholds, i.e. each classification will have a different power threshold value, indicating a required comparison of power to thresholds to know how to set classifications for audio items]).
Lyren, Falk, Jensen, Kim, and Rasmussen are considered analogous art within speech analysis/recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk, further in view of Jensen, further in view of Kim to incorporate the teachings of Rasmussen, because of the novel way to classify audio signals based on frequency component bands to remove acoustic shock in generated audio, improving attenuation of undesired content in audio streams (Rasmussen, [0002]-[0006]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Falk, further in view of Jensen, further in view of Goesnar et al. (US-20160035367-A1), hereinafter Goesnar.
Regarding claim 7, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 5.
Lyren in view of Falk, further in view of Jensen does not disclose:
wherein the two or more spatial components comprise a diffuse component and a direct component.
Goesnar discloses:
wherein the two or more spatial components comprise a diffuse component and a direct component ([Fig. 4], [0042] the system 100 may be configured to provide both a representation of the reverberation of the captured audio (for spatial/multi-channel delivery), as well as a clean signal, [0047] FIG. 4 is a graph of the power of the speech signals of FIG. 2 and the power of the combined speech and reverberation signals of FIG. 3 [Representation of captured audio as reverberation, i.e. a diffuse component, and a clean signal, i.e. a direct component, wherein Fig. 4 shows both of these representations of components]).
Lyren, Falk, Jensen, and Goesnar are considered analogous art within speech recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk, in view of Jensen to incorporate the teachings of Goesnar, because of the novel way to provide representations of both reverberations s (Goesnar, [0042]).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Falk, further in view of Jensen, further in view of Sharma et al. (US-20180211673-A1), hereinafter Sharma.
Regarding claim 11, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren in view of Falk, further in view of Jensen does not disclose:
wherein determining whether to perform dereverberation on the input audio signal is based on a classification of a second input audio signal that preceded the input audio signal.
Sharma discloses:
wherein determining whether to perform dereverberation on the input audio signal is based on a classification of a second input audio signal that preceded the input audio signal ([0050] More generally, classifier output obtained from analysis of an earlier part of an audio stream may be used to predict audio attributes of a later part of the same audio stream, [0055] derive audio classification from environmental (e.g., sensed attributes of the site or venue) and historical data of previously classified audio segments to predict the attributes of the current audio segment [Division of an input audio signal into segments, wherein the segments are time-based (see [0022]), indicates those segments each represent distinct audio signals, further indicating classification of a first audio signal based on a preceding second audio signal, i.e. a prediction based on historical classifications or preceding segments of the audio stream]).
Lyren, Falk, Jensen, and Sharma are considered analogous art within speech recognition/analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk, further in view of Jensen to incorporate the teachings of Sharma, because of the novel way to implement a feedback loop to measure audio quality and monitor audio classifications of audio to be used as predictions for coming audio, allowing for adaptation of audio to be performed at or near real-time (Sharma, [0055]).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Falk, further in view of Jensen, further in view of Moritz et al. (“An Auditory Inspired Amplitude Modulation Filter Bank for Robust Feature Extraction in Automatic Speech Recognition”), hereinafter Moritz.
Regarding claim 23, Lyren in view of Falk, further in view of Jensen discloses: the method of claim 1.
Lyren in view of Falk, further in view of Jensen does not disclose:
wherein the high modulation frequency portion of the two-dimensional acoustic-modulation frequency spectrum of the input audio signal corresponds to a portion of the two-dimensional acoustic-modulation frequency spectrum with modulation frequencies above 10 Hertz (Hz).
Moritz discloses:
wherein the high modulation frequency portion of the two-dimensional acoustic-modulation frequency spectrum of the input audio signal corresponds to a portion of the two-dimensional acoustic-modulation frequency spectrum with modulation frequencies above 10 Hertz (Hz) ([pg. 1927, col. 2, para. 3] a frequency selective amplitude modulation filter bank (AMFB) that is used in a quantitative model to describe data from psychophysical modulation-detection and modulation-masking experiments. This AMFB has two regions with different scaling of the filter properties. The first region ranges from 0 to 10 Hz and exhibits a constant absolute filter BW of 5 Hz. The second area is defined for frequencies between 10 and 1000 Hz and has a constant relative BW to modulation center frequency ratio with a constant value of 2, i.e. the characteristic frequency of a filter corresponds to twice its BW, [Describing modulation detection via two distinct frequency component ranges, in view of the previously cited frequency band-segmented ratio of Falk, indicates the identified frequency ranges of Mortiz could be used as the bounds of the components of the ratio of Falk as they both describe modulation components. [0045] and [0073] of the instant app describe how amplitude modulation is relevant to dereverberation]).
Lyren, Falk, Jensen, and Moritz are considered analogous art within speech recognition in reverberant signals. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Falk, further in view of Jensen to incorporate the teachings of Moritz, because of the novel way to process received speech using an amplitude modulation filter bank having a symmetrical structure for feature extraction in automatic speech recognition systems, allowing for high robustness against room reverberation in the speech to be recognized (Moritz, Abstract).
Claim(s) 13, 17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren et al. (US-20190387343-A1), hereinafter Lyren, in view of Kim et al. (US-20200221242-A1), hereinafter Kim.
Regarding claim 13, Lyren discloses: a method for classifying input audio signal as one of at least two media type ([0072] receives the audio input 240 and classifies this audio input as being either non-speech audio or speech audio), comprising:
receiving an input audio signal ([0044] system 100 obtains, receives, or retrieves audio input 150).
Lyren does not disclose:
upmixing the received input audio signal to generate an upmixed audio signal; and,
separating the input audio signal into two or more spatial components based on the upmixed audio signal.
Kim discloses:
upmixing the received input audio signal to generate an upmixed audio signal ([0059] upmix an input audio signal of stereo channel into an audio signal of multichannel); and,
separating the input audio signal into two or more spatial components based on the upmixed audio signal ([0060] multi-channel signals can be easily synthesized using the primary component analysis, [0404] the left and right channels have high correlation with the same signal, the left and right channels can be decomposed into primary component, [Upmixing into a multi-channel signal, wherein the channels can be left and/or right, indicates the components to be spatial in view of the other front, center, and/or woofer channels of the multi-channel audio ([0404])]).
Lyren and Kim are considered analogous art within speech analysis/recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren to incorporate the teachings of Kim, because of the novel way to upmix downmix stereo audio to a multichannel audio signal, improving/reducing spatial distortion (Kim, Abstract).
Lyren further discloses:
wherein the two or more spatial components comprise one of:
a side channel and a center channel ([The examiner would like to note that due to the disjunctive nature of the claim, this element does not require a mapping]);
a direct component and a diffuse component ([The examiner would like to note that due to the disjunctive nature of the claim, this element does not require a mapping]); or
two or more spatial components resulting from spatial separation of the input audio responsive to determining the input audio signal comprises stereo audio ([0035] For example, the music is convolved to localize to one or more SLPs (e.g., left and right audio channel SLPs), [Left and right channels track to two spatial components resulting from spatial separation in view of the previously cited [0154] which discloses left and right channels to be stereo]);
classifying each of the two or more spatial components as one of the at least two media types ([0151] a track and/or segment can be defined as speech or voice, music, speech with music, noise, gaming sounds, animal sounds, machine generated sounds, or other types of sound [Wherein a track/segment represents a portion of a larger audio signal, in view of the previously disclosed spatial segmentation for classification of Lyren (see [0154]), indicating classification of spatial components. Further, media types of “speech” and “non-speech” are represented through “speech or voice” and “music” as respective examples of speech and non-speech classifications]); and
classifying the media type of the input audio signal by combining classifications of each of the two or more spatial components ([Fig. 7, 700], [0149] The information includes, but is not limited to, one or more of a file format of the audio, a classification or type or source of the audio (e.g., a telephone call, a radio transmission, a television show, a game, a movie, audio output from a software application, etc.), monophonic, stereo, or binaural… a subject matter of the content of the audio, an identify of voices or sounds or speakers in the audio, music in the audio input, noise in the audio input [Classification of the source of the audio based on content, tracking to a media type, wherein individual tracks within the audio file receive classifications, indicating the classification of the audio type/source is based on the individual track level classifications, i.e. if the tracks consist of music, dialogue, and background noise, the classification media type would be “movie”]).
Regarding claim 17, Lyren in view of Kim discloses: the method of claim 13.
Lyren further discloses:
wherein classifying the media type of the input audio signal comprises separating the input audio signal into a vocal component and a non-vocal component ([0048] divides the audio input into speech segments and non-speech segments, [0151] a track and/or segment can be defined as speech or voice, music, speech with music, noise, gaming sounds, animal sounds, machine generated sounds, or other types of sound [Defining tracks, wherein the tracks comprise the input audio, to be specific to voice and other categories indicates a separation of vocal and non-vocal components in the input audio]).
Regarding claim 19, Lyren in view of Kim discloses: the method of claim 17.
Lyren further discloses:
wherein classifying the media type of the input audio signal comprises:
classifying the vocal component as one of: 1) speech ([0151] a track and/or segment can be defined as speech or voice); or 2) non-speech ([0151] a track and/or segment can be defined as… animal sounds [Animal sounds tracks to a non-speech classification]);
classifying the non-vocal component as one of: 1) music ([0151] a track and/or segment can be defined as… music); or 2) non-music ([0151] a track and/or segment can be defined as… machine generated sounds [Defining machine generated sounds as distinct from music indicates they are non-music sounds]);
wherein the media type of the input audio signal is classified by combining the classification of the vocal component and the classification of the non-vocal component ([Fig. 7, 700], [0149] The information includes, but is not limited to, one or more of a file format of the audio, a classification or type or source of the audio (e.g., a telephone call, a radio transmission, a television show, a game, a movie, audio output from a software application, etc.)…a subject matter of the content of the audio, an identify of voices or sounds or speakers in the audio, music in the audio input, noise in the audio input, [Classification of the subject matter of the audio, tracking to a media type, wherein individual tracks within the audio file receive classifications, indicating the classification of the audio type/source is based on the individual track level classifications, i.e. if the vocal tracks consist of dialogue and dialogue over music and the non-vocal tracks consists of background noise and music, the classification media type could be assumed to be “movie”]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Kim, further in view of Rasmussen.
Regarding claim 14, Lyren in view of Kim discloses: the method of claim 13.
Kim further discloses:
wherein the two or more spatial components comprise the center channel and the side channel ([0329] one side channel, [0330] channels such as the front, center, woofer, rear, and upstream channels); and,
calculating a power of the side channel ([0326] the ambient component signal may represent a component having a low correlation between two channels, such as a sound reflected by various paths or a reverberation sound, [0358] The second feature extractor 1045 may extract a feature such as… a power of the ambient component signal [An ambient component representing reverberation between two channels indicates at least one channel could be a side channel]).
Lyren in view of Kim does not disclose:
classifying the side channel in response to determining that the power of the side channel exceeds a threshold.
Rasmussen discloses:
classifying the side channel in response to determining that the power of the side channel exceeds a threshold ([0043] The threshold controller 63 preferably comprises a mapper 69 that maps each predefined audio signal class into an individual frequency-dependent level threshold T.sub.f,k, which may further depend on one or more signal parameters, such as e.g. frequency, bandwidth, level, power and/or energy, indicated in the one or more individual evaluation signals [In view of the previously defined side channel of Kim, indicating the mapping operation could be applied to the side channel, i.e. signal, without a change in functionality. Mapping audio into classes based on a threshold which is dependent upon power indicates the classification is power based, as it relates to thresholds, i.e. each classification will have a different power threshold value, indicating a required comparison of power to thresholds to know how to set classifications for audio items]).
Lyren, Kim, and Rasmussen are considered analogous art within speech analysis/recognition. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Kim to incorporate the teachings of Rasmussen, because of the novel way to classify audio signals based on frequency component bands to remove acoustic shock in generated audio, improving attenuation of undesired content in audio streams (Rasmussen, [0002]-[0006]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Kim, further in view of Sharma.
Regarding claim 15, Lyren in view of Kim discloses: the method of claim 13.
Lyren in view of Kim does not disclose:
combining the classifications of each of the two or more spatial components comprises applying a decision fusion algorithm to the classifications of the two or more spatial components.
Sharma discloses:
combining the classifications of each of the two or more spatial components comprises applying a decision fusion algorithm to the classifications of the two or more spatial components ([0035] classifiers can be used in various combinations, [0045] Separated sounds may be input to subsequent classifier stages for further classification by sound source, including audio fingerprint-based recognition. For watermark embedding, this enables the classifier to separately classify different sounds that are combined in the input audio, [0203] The audio classifiers, perceptual models and quantitative quality measures of a watermark application can be implemented using various combinations of these techniques, tuned to classify audio, [Classifying overall audio based on a combination of individual classifications indicates the individual classifications to be fused using a decision fusion algorithm in view of the previously disclosed classifications of Lyren based on spatial component definitions for determining an audio’s overall classification]).
Lyren, Kim, and Sharma are considered analogous art within speech recognition/analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Kim to incorporate the teachings of Sharma, because of the novel way to implement a feedback loop to measure audio quality and monitor audio classifications of audio to be used as predictions for coming audio, allowing for adaptation of audio to be performed at or near real-time (Sharma, [0055]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyren in view of Kim, further in view of Baque et al. (US-20200152222-A1), hereinafter Baque.
Regarding claim 18, Lyren in view of Kim discloses: the method of claim 17.
Lyren in view of Kim does not disclose:
wherein classifying each of the two or more spatial components comprises determining a confidence level associated with each classification, and
wherein combining the classifications of each of the two or more spatial components is based on the confidence level associated with each classification.
Baque discloses:
wherein classifying each of the two or more spatial components comprises determining a confidence level associated with each classification ([0035] determine the probability of a component belonging to one of the two classes, and thus to determine the N direct sound sources corresponding to the N components classified into the first class, [0061] a module for classifying the components of the set of M components into two classes of components, a first class of N components called direct components corresponding to the N direct sound sources and a second class of M−N components called reverberant components, using a calculation of probability of belonging to one of the two classes, [0139] a probability of belonging to a first class of direct components or a second class of reverberant components is calculated for a pair of components, [Wherein direct and reverberant sound sources track to spatial components of the same sound source as the reverberations are of the original sound. The examiner asserts that a probability of classification is a confidence of classification]), and
wherein combining the classifications of each of the two or more spatial components is based on the confidence level associated with each classification ([0196] A likelihood calculation module 832 makes it possible to determine, in one embodiment, the most probable combination of the classifications of the M components by way of a likelihood value calculation depending on the probabilities calculated at the module 831 and for the possible combinations, [Considering the M components to be comprised of direct and reverberant sound sources as previously cited, indicating the combination of classifications of the M components to be based on the probabilities, i.e. confidences, for each component]).
Lyren, Kim, and Baque are considered analogous art within audio source separation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Lyren in view of Kim to incorporate the teachings of Baque, because of the novel way to calculate descriptors which are indicative of statistical relationships between components of an obtained multichannel audio to be used for classification, improving the ability to identify which components are direct and which are reverberations which degrade audio quality and/or mislead beamforming algorithms (Baque, [0030]-[0035]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Atlas et al. (US-20030185411-A1) discloses “The speech of two or more simultaneous speakers (or other simultaneous sounds) conveyed in a single channel are distinguished. Joint acoustic/modulation frequency analysis and display tools are used to localize and separate sonorant portions of multiple-speakers' speech into distinct regions using invertible transform functions. For example, the regions representing one of the speakers are set to zero, and the inverted modified display maintains only the speech of the other speaker. A combined audio signal is manipulated using a base acoustic transform, followed by a second modulation transform, which separates the combined signals into distinguishable components” (abstract). See [0040]-[0051].
Falk et al. (US-10779769-B2) discloses “There is described a method for evaluating a level of noise in a biosignal, the method comprising: receiving a time signal representative of a biological activity, the time signal comprising a biological activity component and a noise component; determining a modulation spectrum for the time signal, the modulation spectrum representing a signal frequency as a function of a modulation frequency; from the modulation spectrum determining a first amount of modulation energy corresponding to the biological activity component and a second amount of modulation energy corresponding to the noise component determining an indication of the level of noise using the first and second amounts of modulation energy; and outputting the indication of the level of noise.” (abstract). See entire document.
Huang et al. (US-20160005415-A1) discloses “An audio signal processing apparatus and an audio signal processing method thereof are provided. The audio signal processing apparatus is configured to receive an audio signal and divide the audio signal into a plurality of frames. The audio signal processing apparatus is also configured to apply Fourier Transform on each of the frames to obtain a plurality of acoustic spectra. The audio signal processing apparatus is also configured to apply Fourier Transform again on each of component combinations corresponding to respective acoustic frequencies in these acoustic spectra to obtain a two-dimensional joint frequency spectrum. The two-dimensional joint frequency spectrum has an acoustic frequency dimension and a modulation frequency dimension” (abstract). See entire document.
Fukuda et al. (US-20200184995-A1) discloses “A technique for detecting a signal tone in an audio signal is disclosed. A determination is made as to whether a peak modulation frequency in the audio signal is in a specific range or not to obtain a determination result. A measure regarding a modulation spectrum of the audio signal is calculated. The measure is calculated based on at least components of the modulation spectrum above a specific limit of modulation frequency. By using the determination result and the measure regarding the modulation spectrum, a judgement is done as to whether the audio signal contains a signal tone or not” (abstract). See entire document.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE JOHN WITHEY whose telephone number is (703)756-1754. The examiner can normally be reached Monday - Friday, 8am-5pm.
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/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655
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