DETAILED ACTION
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 .
Claim Objections
Claims 1, 5 and 10 are objected to because of the following informalities.
Claim 1 is objected to because the on line 5, “o’n” contains a typographical error. The apostrophe should be deleted.
Claim 5 is objected to because, on line 2, phrase “is obtained by according to the following steps” is grammatically improper. Replace “is obtained by according to” with “is obtained according to”.
Claim 10 is objected to because, on lines 4-5, the phrase “configured to reverberation the reverberation input signal” is grammatically improper. “Reverberation” should be amended to “configured to reverberate”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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 1,9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over US20220215821 (Zheng), hereinafter US’821, US20150066481 (Terrell), hereinafter US’481, US20160111108 (Erdogan), hereinafter US’108.
Regarding claim 1, US’821 discloses ‘An audio reverberation method (US’821, ¶[0028]:”the electronic device 101 usually reverberates the acquired vocal signals artificially”; ¶[0031]), comprising: preprocessing an input audio signal to obtain a reverberation input signal (US’821, Fig 2 , ¶[0034]:”In 201, an accompaniment audio signal and a vocal signal of a current to-be-processed musical composition are acquired”; ¶[0036]);
reverbing the reverberation input signal to generate an initial reverberation audio signal of a target scene (US’821, ¶¶[0023],[0028],[0031],[0036]: artificially reverberating the acquired vocal signal to produce an adaptive Karaoke sound effect);
performing audio content analysis o'n the input audio signal to obtain an audio content feature of the input audio signal (US’821, ¶[0029]:transforming accompaniment-audio frames from the time domain to the frequency domain, obtaining amplitude information for the frames, calculating frequency-domain richness, obtaining a number of beats within a specified duration, and a speed rhythm);
determining a content-adaptive masking matrix based on the audio content feature (US’821, ¶¶[0031], [0035], [0037]-[0039]: determining reverberation-intensity parameter values based on the accompaniment type, rhythm speed and performance score and combining those parameter values to determine a target reverberation-intensity parameter value);
applying the content-adaptive reverberation weight to the reverberation processing to ‘obtain a content-adaptive reverberation signal (US’821, ¶¶[0031],[0036]-[0037]:directing an artificial-reverberation algorithm to control the magnitude of the reverberation according to the target reverberation-intensity parameter value)
US’821 does not expressly disclose ‘performing weighted mixing on the content-adaptive masking matrix and the initial reverberation audio signal to obtain a content-adaptive reverberation signal;
and performing weighted mixing on the content-adaptive reverberation signal and the reverberation input signal according to a preset ratio to obtain a final reverberation audio signal.
However, US’481 discloses reverbing a reverberation input signal to generate ‘the initial reverberation audio signal (US’481, Fig. 23, [0240], [0245]:routing music signals to common music-reverberation processor 726 that applies a reverberation effect and produces a music-reverberation signal at 730e).
US’481 discloses performing weighted mixing on the reverberation signal and the input audio according to a preset ration to obtain a final audio signal (US’481, Fig. 23, ¶¶[0240]-[0242], [0249]: controlling the amount of an audio signal routed to the music-reverberation processor using a corresponding gain parameter, controlling the overall reverberation intensity and output gain, and recombining the reverberation-processed signals with the remaining signals 722 to produce the audio file)
US’108 discloses determining a ‘content-adaptive masking matrix’ based on an audio-content feature (US’108, ¶¶[0009]-[0011]:combining extracted audio/speech-recognition features with spectral features and processing the features using a trained recurrent neural network to produce a mask having respective values for time-frequency bins)
and performing weighted mixing on the content-adaptive and an audio signal to obtain a content-adaptive output signal (US’108, ¶[0011], ¶[0027], ¶[0049]:multiplying the time-frequency mask by the STFT representation of the audio signal to obtain the weighted output-signal STFT).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use US’481’s separately controllable reverberation path and recombination in US’821’s content-adaptive reverberation method to permit independent control of the reverberation contribution when recombining the reverberation and input signals. It would have been further obvious to implement Us’821’s frame-based , frequency-domain reverberation weights using US’108’s time-frequency masking technique to provide time- and frequency-selective control of the separately generated reverberation signal according to the detected musical content, improving control over reverberation intensity across changing spectral and temporal portions of the audio.
Regarding claim 9, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 further comprises ‘wherein, when the input audio signal comprises a plurality of audio content features (US’821, Figs. 4-5, ¶¶[0031], [0035], [0037]-[0039]: identifies first, second, and third reverberation-intensity parameter values corresponding to multiple audio-content features), the determining a content-adaptive masking matrix based on the audio content feature (US’821, ¶¶[0031], [0035], [0037]-[0039]: determining reverberation-intensity parameter values based on the accompaniment type, rhythm speed and performance score and combining those parameter values to determine a target reverberation-intensity parameter value)
US’821 does not expressly disclose ‘comprises: determining a reverberation masking matrix corresponding to one of the audio content features respectively; and combining the reverberation masking matrixes corresponding to the audio content features to obtain the content-adaptive masking matrix of the input audio signal.
US’821 instead teaches the known technique of separately determining respective reverberation-intensity parameter values for multiple audio-content features-including accompaniment type, rhythm speed, and singer-performance score, and combining the separately weighted parameter values to obtain a target reverberation-intensity parameter value (US’821, Fig. 5, ¶¶[0035], [0040]-[0045],[0085] Fig. 5)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to apply US’832’s multi-feature reverberation technique to the time-frequency masking-matrix framework of the modified method of US’821 by determining a respective matrix for each audio-content feature and combining the matrices element-by-element. This modification permits the respective effects of the different audio-content features to be combined at each time-frequency point, which provides more precise content-adaptive control of the reverberation across the input audio signal. Such application would have yielded the predictable result of a single content-adaptive masking matrix reflecting the combined influence of the multiple audio-content features.
Regarding claim 10, US’821 discloses ‘An audio reverberation system (US’821, Figs. 8-10, ¶[0098]: reverberation processing through an acquiring module 801, a determining module 802, and a processing module 803, and alternatively through ¶[0112]:Fig. 9, an electronic device), comprising: a preprocessing module configured to preprocess an input audio signal to obtain a reverberation input signal;
a reverberation generation module configured to reverberation the reverberation input signal to generate an initial reverberation audio signal of a target scene;
an audio content analysis module configured to perform audio content analysis on the input audio signal to obtain an audio content feature of the input audio signal;
a content-adaptive masking module configured to determine a content-adaptive masking matrix based on the audio content feature;
a content-adaptive reverberation module configured to perform weighted mixing on the content-adaptive masking matrix and the initial reverberation audio signal to obtain a content-adaptive reverberation signal;
and a mixing module configured to perform weighted mixing on the content-adaptive reverberation signal and the reverberation input signal according to a preset ratio to obtain a final reverberation audio signal.
The modules recited in claim 10 perform functions corresponding to the respective method steps of claim 1. Therefore, US’821, US’481, and US’108 disclose the limitations of claim 10 for the same reasons set forth above regarding claim 1. It would have been obvious to implement those previously discussed method steps as processor-executed functional modules in US’821’s electronic device because US’821 teaches both modular and processor-based implementations of its audio-processing method, yielding the predictable result of a system configured to perform the method.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over US’821, in view of US’481 and US’108, and in further view of US20230069729 (Torno), hereinafter US’729.
Regarding claim 2, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 further discloses ‘and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal (US’821, ¶¶[0029]-[0031], [0038]-[0039]:accompaniment type, frequency-domain richness, and rhythm speed by transforming accompaniment-audio frames from the time domain to the frequency domain, obtaining amplitude information, calculating frequency-domain richness, and determining a number of beats in a duration)
US’821 does not expressly disclose ‘wherein the audio content feature comprises a music style;
comprises: obtaining the music style of the input audio signal based on an audio tag of the input audio signal;
or obtaining an audio feature based on a music spectrum of the input audio signal, and inputting the audio feature into a pre-trained music classification model to obtain the music style of the input audio signal, wherein the music classification model is trained by using audio features and corresponding classification tags.
However, US’729 discloses ‘wherein the audio content feature comprises a music style (US’729, ¶¶[0129]-[0131]:”typical profile as a function of information about the particular musical style of a track…”);
comprises: obtaining the music style of the input audio signal based on an audio tag of the input audio signal (US’729, ¶[0130]:”The determination of the type of music can also be done via the information contained in the music file (ID3 tag for the MP3 format for example).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to obtain US’821’s audio-content feature from the input audio signal’s music-style tag, as taught by US’729, because US’729 teaches that an ID3 tag provides information identifying the music type and permits automatic selection of settings corresponding to the identified genre. The modification would have provided a direct computationally efficient technique for obtaining the music-style content feature used to adapt the reverberation treatment.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over US’821, in view of US’481 and US’108, and in further view of US20200020349 (Disch), hereinafter US’349.
Regarding claim 3, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 further discloses ‘wherein the audio content feature comprises drumbeat intensity (US’821, ¶¶[0029],[0075]-[0079], [0076]-[0077]:“acquiring a number of beats…within a specified duration”, “the target duration is preset”, determining a rhythm speed from the number of beats within a specified duration, such as beats per minute, and using that value to determine reverberation intensity);
and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal comprises: obtaining the drumbeat intensity of the input audio signal based on a number of the drumbeats in a preset duration (US’821, [0029]: acquiring the number of beats in an audio signal within a specified duration and calculating the rhythm and speed from that number; [0076]-[0077]; [0078]-0079]: resulting beats-per-minute value is used to calculate the reverberation intensity).
US’821 does not expressly disclose ‘and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal comprises: determining abrupt change points of the input audio signal based on a note onsets detection scheme by using energy or spectrum change information, taking the abrupt change points whose abrupt change degrees are greater than a preset abrupt change threshold as drumbeats, and obtaining the drumbeat intensity of the input audio signal based on a number of the drumbeats in a preset duration;
or inputting a multi-frame spectrum of the input audio signal into a pre-trained drumbeat detection model to obtain probabilities of respective time points corresponding to drumming sounds, taking the time points with the probabilities greater than a preset probability threshold as drumbeats, and obtaining the drumbeat intensity of the input audio signal based on a number of the drumbeats in a preset duration.
However US’349 discloses ‘and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal comprises: determining abrupt change points (US’349, ¶¶[0180]-[0182]: detected transient-onset peaks) of the input audio signal based on a note onsets detection scheme (US’349, ¶¶[0144], [0147]-[0149], Fig. 10C: onset picker finds local maxima or peaks in the detection function and determines the transient-onset frame indices) by using energy or spectrum change information (US’349, Fig. 10B, ¶¶[0145]-[0146]: summing of energy values; summing the filtered signals), taking the abrupt change points whose abrupt change degrees are greater than a preset abrupt change threshold as drumbeats (US’349, Fig. 12.7 (b), ¶¶[0180]-[0182]: transient onsets are characterized by a steep or abrupt increase in signal energy; detected transient-onset peaks correspond to abrupt change points).
or inputting a multi-frame spectrum of the input audio signal into a pre-trained drumbeat detection model to obtain probabilities of respective time points corresponding to drumming sounds, taking the time points with the probabilities greater than a preset probability threshold as drumbeats, and obtaining the drumbeat intensity of the input audio signal based on a number of the drumbeats in a preset duration.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to us US’349’s percussive-onset detection procedure as US’821’s beat-analysis algorithm to identify threshold-qualified beat onsets and calculate the number of beats within US’821’s preset duration, thereby providing a reliable beat-intensity value for adaptive reverberation.
Claims 4-5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over US’821, in view of US’481 and US’108, and in further view of US20190043491 (Kupryjanow), hereinafter US’491.
Regarding claim 4, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 (in view of US’481 and US’108) does not expressly disclose ‘wherein the audio content feature comprises a reverberation degree; and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal comprises: inputting a signal spectrum of the input audio signal into a pre-trained de-reverberation model to obtain a time-frequency masking matrix corresponding to de-reverb;
or performing dry sound and wet sound separation on the signal spectrum of the input audio signal, and taking a ratio of a signal spectrum of dry sounds obtained by separation to the signal spectrum of the input audio signal as a time-frequency masking matrix; wherein the time-frequency masking matrix is used to represent the reverberation degree of each time-frequency point.
However, US’491 discloses ‘wherein the audio content feature comprises a reverberation degree (US’349, Fig. 1-2, ¶¶[0017]-[0019]: processing an audio signal containing reverberation and estimating a mask for each time-frequency component);
and the performing audio content analysis on the input audio signal to obtain the audio content feature of the input audio signal comprises: inputting a signal spectrum of the input audio signal into a pre-trained de-reverberation model to obtain a time-frequency masking matrix corresponding to de-reverb (US’491, Fig. 2, ¶¶[0018]-[0019], ¶¶[0023]-[0026]: a trained recurrent neural network configured to estimate time-frequency masks for processing an audio signal affected by reverberation);
or performing dry sound and wet sound separation on the signal spectrum of the input audio signal, and taking a ratio of a signal spectrum of dry sounds obtained by separation to the signal spectrum of the input audio signal as a time-frequency masking matrix;
wherein the time-frequency masking matrix is used to represent the reverberation degree of each time-frequency point (US’491, Figs 2, 5 and 10, ¶¶[0025]-[0026], [0049]: each mask value represents, on a time-frequency-point basis, the relative presence the clean or direct signal compared with the undesired components; Fig. 7, ¶¶[0040]-[0041]: the ratio is calculated separately for the spectral components, it provides a mask value reflecting the degree to which each time-frequency component corresponds to clean/direct sound).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use US’491’s trained time-frequency-mask estimation technique to analyze the input audio signal in the combined method of US’821. Doing so would estimate and quantify the existing clean-to-reverberant content at individual time-frequency points, thereby permitting more accurate content-adaptive control and reducing the likelihood of excessive reverberation.
Regarding claim 5, US’821 (in view of US’481, US’108 & US’491) discloses ‘The audio reverberation method as described in claim 4, as discussed above.
US’491 further teaches ‘wherein the pre-trained de-reverberation model is obtained by according to the following training steps (US’491, Fig 7., ¶¶[0039]-[0043]: RNN training circuit 700 for training a recurrent neural network to generate and estimated time-frequency mask from audio affected by reverberation):
acquiring a clean audio and a reverberation audio thereof, and training the de-reverberation model by using a reverberation audio spectrum of the reverberation audio as an input of the de-reverberation model and the time-frequency masking matrix corresponding to the de-reverberation as an output of the de-reverberation model (US’491, Fig. 7, ¶¶[0039]-[0040]: acquiring clean audio and generating/acquiring corresponding reverberation audio from that clean audio);
or acquiring a clean audio and a reverberation audio thereof, generating a first target artificial reverberation audio based on the clean audio, and generating a second target artificial reverberation audio based on the reverberation audio;
and training the de-reverberation model by using the reverberation audio as an input of the de-reverberation model (US’491, Fig. 7, ¶[0040]:the feature-extraction circuit 740 generates a frequency-domain representation X(k) of the input signal; the representation is supplied to RNN circuit 510, which generates an estimated time-frequency mask; X(k) includes the spectrum of the corresponding reverberant audio) and by using a time-frequency masking matrix corresponding to a ratio of the first target artificial reverberation audio to the second target artificial reverberation audio as an output of the de-reverberation model (US’491, Fig. 7, ¶¶[0040-[0042]: the ideal ratio mask (IRM) is used as the training target model for RNN, while RNN produces the corresponding estimated time-frequency mask).
Regarding claim 8, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 further discloses ‘and the determining a content-adaptive masking matrix based on the audio content feature (US’821, ¶¶[0031], [0035], [0037]-[0039]: determining reverberation-intensity parameter values based on the accompaniment type, rhythm speed and performance score and combining those parameter values to determine a target reverberation-intensity parameter value).
a reverberation weighted weight related to a reverberation degree at the current time-frequency point (US’821,¶¶ [0031],[0035], [0037]-[0039]: determining a content-adaptive masking matrix based on an audio-content feature using the weighting to control amount of applied reverberation)
US’821 does not expressly disclose ‘wherein the audio content feature comprises a reverberation degree, the reverberation degree being represented by a time-frequency masking matrix;
comprises: determining, based on a masking value in the time-frequency masking matrix corresponding to a current time-frequency point, a reverberation weighted weight related to a reverberation degree at the current time-frequency point by using a monotonically increasing reverberation weight calculation function.
However, US’491 discloses ‘wherein the audio content feature comprises a reverberation degree (US’491, Figs. 2, 4, and 10, ¶¶[0022], [0024], [0034]-[0035], [0048]: audio signals using dereverberation circuit 230), the reverberation degree being represented by a time-frequency masking matrix (US’491, Fig. 6, ¶[0025]: mask values represent the probability of speech being present at the corresponding time and frequency);
comprises: determining, based on a masking value in the time-frequency masking matrix corresponding to a current time-frequency point (US’491, ¶[0025]: a mask element is assigned to each corresponding time slot and frequency bin; ¶¶[0027], [0036]-[0037], [0049]:t and k are identified as time-slot and frequency-bin indices of each mask element), a reverberation weighted weight related to a reverberation degree at the current time-frequency point by using a monotonically increasing reverberation weight calculation function (US’491, ¶¶[0036]-[0037]: the weighted expression provides and affine increasing mapping when the selected mask-weighting factor is positive; direct and weighted use of the mask value at the corresponding time-frequency point).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use US’491’s direct or proportional mask-value mapping in determining US’821’s content-adaptive reverberation weight because the mapping increases the applied weight as the clean/direct-signal mask value increase, thereby providing a monotonically increasing reverberation-weight calculation function and avoiding excessive reverberation.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over US’821, in view of US’481 and US’108, and in further view of US20200081683 (Cremer), hereinafterUS’683.
Regarding claim 6, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 (in view of US’481 and US’108) further discloses ‘wherein the audio content feature comprises a music style (US’481, Fig. 8D-9, ¶¶[0108]-[0111], [0110]: automatically determining genre settings from source detection and configuring audio processors with different parameter settings; Figs. 16 and 18, ¶[0186]: using an SVM to classify an input audio file into a music genre);
‘and suppression coefficients at different frequencies (US’481, Fig. 8C, ¶¶[0107]-[0108]: frame-by-frame classification may produce an array of frequency-related gain boosts and cuts, which are supplied to the audio-processing modules), a reverberation weighted weight related to a music style at the current time point (US’481, Figs. 8D-9, ¶¶[0108]-[0110]:selecting different processor parameters and presets depending on the detected genre; Fig. 23, ¶¶[0236]-[0242]: music-reverberation processor 726; ¶[0242]: using a corresponding gain parameter to determine how much signal is routed to the reverberation);
and the determining the content-adaptive masking matrix based on the audio content feature (US’821, ¶¶[0031], [0035], [0037]-[0039]: determining reverberation-intensity parameter values based on the accompaniment type, rhythm speed and performance score and combining those parameter values to determine a target reverberation-intensity parameter value).
US’821 (in view of US’481 and US’108) does not expressly disclose ‘comprises: determining, based on a music style detection probability
However, US’683 discloses ‘comprises: determining, based on a music style detection probability (US’683, Figs. 2-4, ¶¶[0015], [0046]-[0047], [0075], [0113]: audio classifier may output a probability distribution indicating the probabilities that the input audio signal belongs to respective classification groups) at a current time point (US’683, Figs. 4-5, ¶[0021]:performing classification at regular intervals and determining updated gain values based on the current classification).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to determine US’481’s genre-dependent reverberation and frequency-processing weights using US’683’s probability-weighted classification technique. Doing so would have allowed the system to blend the frequency-dependent reverberation settings associated with multiple possible music styles according to the current classification probabilities, thereby providing smoother content-adaptive reverberation.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over US’821, in view of US’481 and US’108, and in further view of WO2023174951 (Weber), hereinafter US’951.
Regarding claim 7, US’821 (in view of US’481 and US’108) discloses ‘The audio reverberation method as described in claim 1, as discussed above.
US’821 further discloses wherein the audio content feature comprises drumbeat intensity (US’821, [0029],[0075]-[0079]: number of beats per preset duration);
and the determining a content-adaptive masking matrix based on the audio content feature (US’821, ¶¶[0031], [0035], [0037]-[0039]: determining reverberation-intensity parameter values based on the accompaniment type, rhythm speed and performance score and combining those parameter values to determine a target reverberation-intensity parameter value) comprises:
However, US’821 does not expressly disclose ‘determining, based on thedrumbeat intensity at a current time point and a corresponding frequency, a reverberation weighted weight related to a drumbeat at the current time point by using a monotonically decreasing reverberation weight calculation function.
WO’951 discloses determining, based on the drumbeat intensity at a current time point (WO’951, p.15, ll. 18-30 and p. 16, ll. 15-30: calculates its spectral transient measure over time and uses time smoothing and short analysis blocks) and a corresponding frequency (WO’951, p. 15, ll. 8-18: calculates transient measure separately for frequency bands), a reverberation weighted weight related to a drumbeat (WO’951, p. 17, ll. 20-38; p. 18, ll. 1-10: converts the transient-dependent scaling value/equivalent reverberation level into a reverb-send gain) at the current time point by using a monotonically decreasing reverberation weight calculation function (WO’951, p. 18, ll. 1-10: the gain-adjustment data exhibit a “monotonic relationship”, approximates that relationship using a third-degree polynomial mapping; p. 17, ll. 35-38: the decreasing direction: stronger transients require lower reverb-send gain)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the content-adaptive reverberation of US’821 (in view of US’481 & US’108) by using WO’951’s frequency-dependent transient measure and monotonically decreasing reverb-gain relationship, such that increasing drumbeat intensity at a current time point and corresponding frequency produces a decreasing reverberation weight. With this modification, US’951 teaches that strong percussive transients produce greater perceived reverberation and less effectively mask reverberation, such that a lower reverberation gain is appropriate (WO’951, p. 14, ll. 27-32, p. 17, ll. 30-38).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20200357369 (Wu) teaches spectrum sub-bands specifically detect bass-drum and snare-drum beat point, weighted spectral values, peak points exceeding the threshold are expressly marked as bass-drum or snare0drum beat points, and threshold detection of snare-drum peaks.
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/NICOLE K GILLESPIE/Examiner, Art Unit 2837
/DEDEI K HAMMOND/Supervisory Patent Examiner, Art Unit 2837
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