Source: http://patents.com/us-7711123.html
Timestamp: 2018-12-16 05:58:01
Document Index: 433079074

Matched Legal Cases: ['application No. 2002248431', 'Application No. 02808144', 'Application No. 02808144', 'application No. 2002307533', 'Application No. 02809542', 'Application No. 02809542', 'Application No. 02', 'Application No. 1308', 'Application No. 02810670', 'Application No. 02810672', 'Application No. 02810672', 'Application No. 02', 'Application No. 02', 'Application No. 01490', 'Application No. 01490', 'Application No. 01490', 'application No. 2002252143', 'Application No. 02810671', 'Application No. 02810671', 'Application No. 02810671', 'Application No. 01487', 'Application No. 01487', 'Application No. 05', 'Application No. 05', 'Application No. 0281671']

US Patent # 7,711,123. Segmenting audio signals into auditory events - Patents.com
United States Patent 7,711,123
Crockett May 4, 2010
Inventors: Crockett; Brett G. (Brisbane, CA)
Appl. No.: 10/478,538
PCT Filed: February 26, 2002
PCT No.: PCT/US02/05999
371(c)(1),(2),(4) Date: November 20, 2003
PCT Pub. No.: WO02/097792
PCT Pub. Date: December 05, 2002
PCT/US02/04317 Feb., 2002
10045644 Jan., 2002
09922394 Aug., 2001
09834739 Apr., 2001
60351498 Jan., 2002
60293825 May., 2001
Current U.S. Class: 381/56 ; 381/80; 381/94.3; 700/94
Current International Class: H04R 29/00 (20060101); G06F 17/00 (20060101); H04B 15/00 (20060101); H04B 3/00 (20060101)
Field of Search: 700/94 381/94.1,94.2,94.3,94.4-94.5,56,80,58,98 704/501,504
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1. A method for dividing each of multiple channels of digital audio signals into auditory events, each of which auditory events tends to be perceived as separate and distinct, comprising detecting every change exceeding a threshold in the spectral profile with respect to time in a frequency domain representation of the audio signal in each of the channels, said detecting being insensitive to changes in amplitude of the overall spectral profile, and in each channel, identifying a continuous succession of auditory event boundaries in the audio signal, in which such every change in the spectral profile with respect to time exceeding a threshold defines a boundary, wherein each auditory event is an audio segment between adjacent boundaries and there is only one auditory event between such adjacent boundaries, each boundary representing the end of the preceding event and the beginning of the next event such that a continuous succession of auditory events is obtained, wherein neither auditory event boundaries, auditory events, nor any characteristics of an auditory event are known in advance of identifying the continuous succession of auditory event boundaries and obtaining the continuous succession of auditory events.
The present application is related to United States Non-Provisional Patent Application Ser. No. 10/474,387, entitled "High Quality Time-Scaling and Pitch-Scaling of Audio Signals," by Brett Graham Crockett, filed Oct. 7, 2003, published as US 2004/0122662 on Jun. 24, 2004, The PCT counterpart application was published as WO 02/084645 A2 on Oct. 24, 2002.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 10/476,347, entitled "Improving Transient Performance of Low Bit Rate Audio Coding Systems by Reducing Pre-Noise," by Brett Graham Crockett, filed Oct. 28, 2003, published as US 2004/0133423 on Jul. 8, 2004, now U.S. Pat. No. 7,313,519. The PCT counterpart application was published as WO 02/093560 on Nov. 21, 2002.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 10/478,397, entitled "Comparing Audio Using Characterizations The present application is also related to United States Non-Provisional Patent Based on Auditory Events," by Brett Graham Crockett and Michael John Smithers. filed Nov. 20, 2003, published a 2004/0172240 on Sep. 2, 2004, now U.S. Pat. No. 7,283,954. The PCT counterpart application was published as WO 02/097790 on Dec. 5, 2002.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 10/474,398, entitled "Method for Time Aligning Audio Signals using Characterizations Based on Auditory Events," by Brett Graham Crockett and Michael John Smithers, filed Nov. 20, 2003, published as US 2004-0148159 on Jul. 29, 2004. The PCT counterpart application was published as WO 02/097791 on Dec. 5, 2002.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 10/591,374, entitled "Multichannel Audio Coding," by Mark Franklin Davis, filed Aug. 31, 2006, published as US/2007/0140499 on Jun. 21, 2007. The PCT counterpart application was published as WO 05/086139 on Sep. 15, 2005.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 10/911,404, entitled "Method for Combining Audio Signals Using Auditory Scene Analysis," by Michael John Smithers, filed Aug. 3, 2004, published as US/2006/0029239 on Feb. 9, 2006. The PCT counterpart application was published as WO 2006/019719 on Feb. 23, 2006.
The present application is also related to United States Non-Provisional Patent Application Ser. No. 11/999,159, entitled "Channel Reconfiguration with Side Information," by Alan Jeffrey Seefeldt, Mark Stuart Vinton and Charles Quito Robinson, filed Dec. 3, 2007.
The present application is also related to PCT Application (designating the U.S.) Ser. No. PCT/2006/028874, entitled "Controlling Spatial Audio Coding Parameters as a Function of Auditory Events," by Alan Jeffrey Seefeldt and Mark Stuart Vinton, filed Jul. 24, 2006. The PCT counterpart application was published as WO 07/016107 on Feb. 8, 2007.
The present application is also related to PCT Application (designating the U.S.), Ser. No. PCT/2007/008313, entitled "Audio Gain Control Using Specific-Loudness-Based Auditory Event Detection." by Brett Graham Crockett and Alan Jeffrey Seefeldt, filed Mar. 30, 2007. The PCT counterpart application was published as WO 2007/127023 on Nov. 8, 2007.
The present invention pertains to the field of psychoacoustic processing of audio signals. In particular, the invention relates to aspects of dividing or segmenting audio signals into "auditory events," each of which tends to be perceived as separate and distinct, and to aspects of generating reduced-information representations of audio signals based on auditory events and, optionally, also based on the characteristics or features of audio signals within such auditory events. Auditory events may be useful as defining the MPEG-7 "Audio Segments" as proposed by the "ISO/IEC JTC 1/SC 29/WG 11."
The division of sounds into units or segments perceived as separate and distinct is sometimes referred to as "auditory event analysis" or "auditory scene analysis" ("ASA"). An extensive discussion of auditory scene analysis is set forth by Albert S. Bregman in his book Auditory Scene Analysis--The Perceptual Organization of Sound, Massachusetts Institute of Technology, 1991, Fourth printing, 2001, Second MIT Press paperback edition.) In addition, U.S. Pat. No. 6,002,776 to Bhadkamkar, et al, Dec. 14, 1999 cites publications dating back to 1976 as "prior art work related to sound separation by auditory scene analysis." However, the Bhadkamkar, et al patent discourages the practical use of auditory scene analysis, concluding that "[t]echniques involving auditory scene analysis, although interesting from a scientific point of view as models of human auditory processing, are currently far too computationally demanding and specialized to be considered practical techniques for sound separation until fundamental progress is made."
There are many different methods for extracting characteristics or features from audio. Provided the features or characteristics are suitably defined, their extraction can be performed using automated processes. For example "ISO/IEC JTC 1/SC 29/WG 11" (MPEG) is currently standardizing a variety of audio descriptors as part of the MPEG-7 standard. A common shortcoming of such methods is that they ignore auditory scene analysis. Such methods seek to measure, periodically, certain "classical" signal processing parameters such as pitch, amplitude, power, harmonic structure and spectral flatness. Such parameters, while providing useful information, do not analyze and characterize audio signals into elements perceived as separate and distinct according to human cognition. However, MPEG-7 descriptors may be useful in characterizing an Auditory Event identified in accordance with aspects of the present invention.
In accordance with aspects of the present invention, a computationally efficient process for dividing audio into temporal segments or "auditory events" that tend to be perceived as separate and distinct is provided. The locations of the boundaries of these auditory events (where they begin and end with respect to time) provide valuable information that can be used to describe an audio signal. The locations of auditory event boundaries can be assembled to generate a reduced-information representation, "signature, or "fingerprint" of an audio signal that can be stored for use, for example, in comparative analysis with other similarly generated signatures (as, for example, in a database of known works).
Bregman notes that "[w]e hear discrete units when the sound changes abruptly in timbre, pitch, loudness, or (to a lesser extent) location in space." (Auditory Scene Analysis--The Perceptual Organization of Sound, supra at page 469). Bregman also discusses the perception of multiple simultaneous sound streams when, for example, they are separated in frequency.
In the case of multiple audio channels, each representing a direction in space, each channel may be treated independently and the resulting event boundaries for all channels may then be ORed together. Thus, for example, an auditory event that abruptly switches directions will likely result in an "end of event" boundary in one channel and a "start of even" boundary in another channel. When ORed together, two events will be identified. Thus, the auditory event detection process of the present invention is capable of detecting auditory events based on spectral (timbre and pitch), amplitude and directional changes.
The subband auditory event information may be used to derive an auditory event signature for each subband. While this would increase the size of the audio signal's signature and possibly increase the computation time required to compare multiple signatures it could also greatly reduce the probability of falsely classifying two signatures as being the same. A tradeoff between signature size, computational complexity and signal accuracy could be done depending upon the application. Alternatively, rather than providing a signature for each subband, the auditory events may be ORed together to provide a single set of "combined" auditory event boundaries (at samples 1024, 1536, 2560, 3072 and 3584. Although this would result in some loss of information, it provides a single set of event boundaries, representing combined auditory events, that provides more information than the information of a single subband or a wideband analysis.
In the case of analyzing multiple audio channels, each channel is analyzed independently and the auditory event boundary information of each may either be retained separately or be combined to provide combined auditory event information. This is somewhat analogous to the case of multiple subbands. Combined auditory events may be better understood by reference to FIG. 3 that shows the auditory scene analysis results for a two channel audio signal. FIG. 3 shows time concurrent segments of audio data in two channels. ASA processing of the audio in a first channel, the top waveform of FIG. 3, identifies auditory event boundaries at samples that are multiples of the 512 sample spectral-profile block size, 1024 and 1536 samples in this example. The lower waveform of FIG. 3 is a second channel and ASA processing results in event boundaries at samples that are also multiples of the spectral-profile block size, at samples 1024, 2048 and 3072 in this example. A combined auditory event analysis for both channels results in combined auditory combined auditory event analysis for both channels results in combined auditory event segments with boundaries at samples 1024, 1536, 2048 and 3072 (the auditory event boundaries of the channels are "ORed" together). It will be appreciated that in practice the accuracy of auditory event boundaries depends on the size of the spectral-profile block size (N is 512 samples in this example) because event boundaries can occur only at block boundaries. Nevertheless, a block size of 512 samples has been found to determine auditory event boundaries with sufficient accuracy as to provide satisfactory results.
FIG. 3A shows three auditory events. These events include the (1) quiet portion of audio before the transient, (2) the transient event, and (3) the echo/sustain portion of the audio transient. A speech signal is represented in FIG. 3B having a predominantly high-frequency sibilance event, and events as the sibilance evolves or "morphs" into the vowel, the first half of the vowel, and the second half of the vowel.
In principle, the processed audio may be digital or analog and need not be divided into blocks. However, in practical applications, the input signals likely are one or more channels of digital audio represented by samples in which consecutive samples in each channel are divided into blocks of; for example 4096 samples (as in the examples of FIGS. 1, 3 and 4, above). In practical embodiments set forth herein, auditory events are determined by examining blocks of audio sample data preferably representing approximately 20 ms of audio or less, which is believed to be the shortest auditory event recognizable by the human ear. Thus, in practice, auditory events are likely to be determined by examining blocks of; for example, 512 samples, which corresponds to about 11.6 ms of input audio at a sampling rate of 44.1 kHz, within larger blocks of audio sample data. However, throughout this document reference is made to "blocks" rather than "subblocks" when referring to the examination of segments of audio data for the purpose of detecting auditory event boundaries. Because the audio sample data is examined in blocks, in practice, the auditory event temporal start and stop point boundaries necessarily will each coincide with block boundaries. There is a trade off between real-time processing requirements (as larger blocks require less processing overhead) and resolution of event location (smaller blocks provide more detailed information on the location of auditory events).
In accordance with an embodiment of one aspect of the present invention, auditory scene analysis is composed of three general processing steps as shown in a portion of FIG. 5. The first step 5-1 ("Perform Spectral Analysis") takes a time-domain audio signal, divides it into blocks and calculates a spectral profile or spectral content for each of the blocks. Spectral analysis transforms the audio signal into the short-term frequency domain. This can be performed using any filterbank, either based on transforms or banks of bandpass filters, and in either linear or warped frequency space (such as the Bark scale or critical band, which better approximate the characteristics of the human ear). With any filterbank there exists a tradeoff between time and frequency. Greater time resolution, and hence shorter time intervals, leads to lower frequency resolution. Greater frequency resolution, and hence narrower subbands, leads to longer time intervals.
The first step, illustrated conceptually in FIG. 6 calculates the spectral content of successive time segments of the audio signal. In a practical embodiment, the ASA block size is 512 samples of the input audio signal. In the second step 5-2, the differences in spectral content from block to block are determined ("Perform spectral profile difference measurements"). Thus, the second step calculates the difference in spectral content between successive time segments of the audio signal. As discussed above, a powerful indicator of the beginning or end of a perceived auditory event is believed to be a change in spectral content. In the third step 5-3 ("Identify location of auditory event boundaries"), when the spectral difference between one spectral-profile block and the next is greater than a threshold, the block boundary is taken to be an auditory event boundary. The audio segment between consecutive boundaries constitutes an auditory event. Thus, the third step sets an auditory event boundary between successive time segments when the difference in the spectral profile content between such successive time segments exceeds a threshold, thus defining auditory events. In this embodiment, auditory event boundaries define auditory events having a length that is an integral multiple of spectral profile blocks with a minimum length of one spectral profile block (512 samples in this example). In principle, event boundaries need not be so limited. As an alternative to the practical embodiments discussed herein, the input block size may vary, for example, so as to be essentially the size of an auditory event.
The locations of event boundaries may be stored as a reduced-information characterization or "signature" and formatted as desired, as shown in step 5-4. An optional process step 5-5 ("Identify dominant subband") uses the spectral analysis of step 5-1 to identify a dominant frequency subband that may also be stored as part of the signature. The dominant subband information may be combined with the auditory event boundary information in order to define a feature of each auditory event.
The following variables may be used to compute the spectral profile of the input block: N=number of samples in the input signal M=number of windowed samples in a block used to compute spectral profile P=number of samples of spectral computation overlap Q=number of spectral windows/regions computed
In general, any integer numbers may be used for the variables above. However, the implementation will be more efficient if M is set equal to a power of 2 so that standard FFTs may be used for the spectral profile calculations. In addition, if N, M, and P are chosen such that Q is an integer number, this will avoid under-running or over-running audio at the end of the N samples. In a practical embodiment of the auditory scene analysis process, the parameters listed may be set to: M=512 samples (or 11.6 ms at 44.1 kHz) P=0 samples (no overlap)
In step 5-1 (FIG. 5), the spectrum of each M-sample block may be computed by windowing the data by an M-point Hanning, Kaiser-Bessel or other suitable window, converting to the frequency domain using an M-point Fast Fourier Transform, and calculating the magnitude of the complex FFT coefficients. The resultant data is normalized so that the largest magnitude is set to unity, and the normalized array of M numbers is converted to the log domain. The array need not be converted to the log domain, but the conversion simplifies the calculation of the difference measure in step 5-2. Furthermore, the log domain more closely matches the nature of the human auditory system. The resulting log domain values have a range of minus infinity to zero. In a practical embodiment, a lower limit can be imposed on the range of values; the limit may be fixed, for example -60 dB, or be frequency-dependent to reflect the lower audibility of quiet sounds at low and very high frequencies. (Note that it would be possible to reduce the size of the array to M/2 in that the FFT represents negative as well as positive frequencies).
Step 5-3 identifies the locations of auditory event boundaries by applying a threshold to the array of difference measures from step 5-2 with a threshold value. When a difference measure exceeds a threshold, the change in spectrum is deemed sufficient to signal a new event and the block number of the change is recorded as an event boundary. For the values of M and P given above and for log domain values (in step 5-1) expressed in units of dB, the threshold may be set equal to 2500 if the whole magnitude FFT (including the mirrored part) is compared or 1250 if half the FFT is compared (as noted above, the FFT represents negative as well as positive frequencies--for the magnitude of the FFT, one is the mirror image of the other). This value was chosen experimentally and it provides good auditory event boundary detection. This parameter value may be changed to reduce (increase the threshold) or increase (decrease the threshold) the detection of events.
For each block, an optional additional step in the processing of FIG. 5 is to extract information from the audio signal denoting the dominant frequency "subband" of the block (conversion of the data in each block to the frequency domain results in information divided into frequency subbands). This block-based information may be converted to auditory-event based information, so that the dominant frequency subband is identified for every auditory event. Such information for every auditory event provides information regarding the auditory event itself and may be useful in providing a more detailed and unique reduced-information representation of the audio signal. The employment of dominant subband information is more appropriate in the case of determining auditory events of full bandwidth audio rather than cases in which the audio is broken into subbands and auditory events are determined for each subband.
TABLE-US-00001 Subband 1 300 Hz to 550 Hz Subband 2 550 Hz to 2000 Hz Subband 3 2000 Hz to 10,000 Hz
TABLE-US-00002 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 (Event Boundaries) 1 1 2 2 2 2 1 1 1 3 3 3 3 3 3 1 1 (Dominant Subbands)
The process of FIG. 5 may be represented more generally by the equivalent arrangements of FIGS. 7, 8 and 9. In FIG. 7, an audio signal is applied in parallel to an "Identify Auditory Events" function or step 7-1 that divides the audio signal into auditory events, each of which tends to be perceived as separate and distinct and to an optional "Identify Characteristics of Auditory Events" function or step 7-2. The process of FIG. 5 may be employed to divide the audio signal into auditory events or some other suitable process may be employed. The auditory event information, which may be an identification of auditory event boundaries, determined by function or step 7-1 is stored mid formatted, as desired, by a "Store and Format" function or step 7-3. The optional "Identify Characteristics" function or step 7-3 also receives the auditory event information. The "Identify Characteristics" function or step 7-3 may characterize some or all of the auditory events by one or more characteristics. Such characteristics may include an identification of the dominant subband of the auditory event, as described in connection with the process of FIG. 5. The characteristics may also include one or more of the MPEG-7 audio descriptors, including, for example, a measure of power of the auditory event, a measure of amplitude of the auditory event, a measure of the spectral flatness of the auditory event, and whether the auditory event is substantially silent. The characteristics may also include other characteristics such as whether the auditory event includes a transient. Characteristics for one or more auditory events are also received by the "Store and Format" function or step 7-3 and stored and formatted along with the auditory event information.
Alternatives to the arrangement of FIG. 7 are shown in FIGS. 8 and 9. In FIG. 8, the audio input signal is not applied directly to the "Identify Characteristics" function or step 8-3, but it does receive information from the "Identify Auditory Events" function or step 8-1. The arrangement of FIG. 5 is a specific example of such an arrangement. In FIG. 9, the functions or steps 9-1, 9-2 and 9-3 are arranged in series.
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