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Timestamp: 2014-03-13 20:38:41
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Matched Legal Cases: ['application No. 2005299410', 'Application No. 03791682', 'Application No. 05818505', 'Application No. 05818505', 'Application No. 182097', 'Application No. 03819918', 'Application No. 02808144', 'Application No. 02808144', 'application No. 2002248431', 'Application No. 165', 'Application No. 03819918', 'art 2', 'Application No. 0702926']

Patent US8144881 - Audio gain control using specific-loudness-based auditory event detection - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsIn one disclosed aspect, dynamic gain modifications are applied to an audio signal at least partly in response to auditory events and/or the degree of change in signal characteristics associated with said auditory event boundaries. In another aspect, an audio signal is divided into auditory events by...http://www.google.com/patents/US8144881?utm_source=gb-gplus-sharePatent US8144881 - Audio gain control using specific-loudness-based auditory event detectionAdvanced Patent SearchPublication numberUS8144881 B2Publication typeGrantApplication numberUS 12/226,698PCT numberPCT/US2007/008313Publication dateMar 27, 2012Filing dateMar 30, 2007Priority dateApr 27, 2006Also published asCA2648237A1, CA2648237C, CN101432965A, CN101432965B, CN102684628A, DE602007011594D1, EP2011234A1, EP2011234B1, US8428270, US20090220109, US20120155659, US20120321096, US20130243222, WO2007127023A1Publication number12226698, 226698, PCT/2007/8313, PCT/US/2007/008313, PCT/US/2007/08313, PCT/US/7/008313, PCT/US/7/08313, PCT/US2007/008313, PCT/US2007/08313, PCT/US2007008313, PCT/US200708313, PCT/US7/008313, PCT/US7/08313, PCT/US7008313, PCT/US708313, US 8144881 B2, US 8144881B2, US-B2-8144881, US8144881 B2, US8144881B2InventorsBrett Graham Crockett, Alan Jeffrey SeefeldtOriginal AssigneeDolby Laboratories Licensing CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (104), Non-Patent Citations (124), Referenced by (6), Classifications (12), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetAudio gain control using specific-loudness-based auditory event detectionUS 8144881 B2Abstract In one disclosed aspect, dynamic gain modifications are applied to an audio signal at least partly in response to auditory events and/or the degree of change in signal characteristics associated with said auditory event boundaries. In another aspect, an audio signal is divided into auditory events by comparing the difference in specific loudness between successive time blocks of the audio signal.
D ⁡ [ t ] = ∑ b ⁢  N NORM ⁡ [ b , t ] - N NORM ⁡ [ b , t - 1 ]  where N NORM ⁡ [ b , t ] = N ⁡ [ b , t ] max b ⁢ { N ⁡ [ b , t ] } . 11. A method according to claim 9 wherein the difference in spectral content between successive time blocks of the audio signal is calculated according to
D ⁡ [ t ] = ∑ b ⁢  N NORM ⁡ [ b , t ] - N NORM ⁡ [ b , t - 1 ]  where N NORM ⁡ [ b , t ] = N ⁡ [ b , t ] avg b ⁢ { N ⁡ [ b , t ] } . 12. A non-transitory computer-readable storage medium encoded with a computer program for causing a computer to perform the method of claim 1.
TECHNICAL FIELD The present invention relates to audio dynamic range control methods and apparatus in which an audio processing device analyzes an audio signal and changes the level, gain or dynamic range of the audio, and all or some of the parameters of the audio gain and dynamics processing are generated as a function of auditory events. The invention also relates to computer programs for practicing such methods or controlling such apparatus.
BACKGROUND ART Dynamics Processing of Audio The techniques of automatic gain control (AGC) and dynamic range control (DRC) are well known and are a common element of many audio signal paths. In an abstract sense, both techniques measure the level of an audio signal in some manner and then gain-modify the signal by an amount that is a function of the measured level. In a linear, 1:1 dynamics processing system, the input audio is not processed and the output audio signal ideally matches the input audio signal. Additionally, if one has an audio dynamics processing system that automatically measures characteristics of the input signal and uses that measurement to control the output signal, if the input signal rises in level by 6 dB and the output signal is processed such that it only rises in level by 3 dB, then the output signal has been compressed by a ratio of 2:1 with respect to the input signal. International Publication Number WO 2006/047600 A1 (�Calculating and Adjusting the Perceived Loudness and/or the Perceived Spectral Balance of an Audio Signal� by Alan Jeffrey Seefeldt) provides a detailed overview of the five basic types of dynamics processing of audio: compression, limiting, automatic gain control (AGC), expansion and gating.
Auditory Events and Auditory Event Detection 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�) and the segments are sometimes referred to as �auditory events� or �audio events.� An extensive discussion of auditory scene analysis is set forth by Albert S. Bregman in his book Auditory Scene Analysts�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.�
A useful way to identify auditory events is set forth by Crockett and Crocket et al in various patent applications and papers listed below under the heading �Incorporation by Reference.� According to those documents, an audio signal is divided into auditory events, each of which tends to be perceived as separate and distinct, by detecting changes in spectral composition (amplitude as a function of frequency) with respect to time. This may be done, for example, by calculating the spectral content of successive time blocks of the audio signal, calculating the difference in spectral content between successive time blocks of the audio signal, and identifying an auditory event boundary as the boundary between successive time blocks when the difference in the spectral content between such successive time blocks exceeds a threshold. Alternatively, changes in amplitude with respect to time may be calculated instead of or in addition to changes in spectral composition with respect to time.
DISCLOSURE OF THE INVENTION Conventional prior-art dynamics processing of audio involves multiplying the audio by a time-varying control signal that adjusts the gain of the audio producing a desired result. �Gain� is a scaling factor that scales the audio amplitude. This control signal may be generated on a continuous basis or from blocks of audio data, but it is generally derived by some form of measurement of the audio being processed, and its rate of change is determined by smoothing filters, sometimes with fixed characteristics and sometimes with characteristics that vary with the dynamics of the audio. For example, response times may be adjustable in accordance with changes in the magnitude or the power of the audio. Prior art methods such as automatic gain control (AGC) and dynamic range compression (DRC) do not assess in any psychoacoustically-based way the time intervals during which gain changes may be perceived as impairments and when they can be applied without imparting audible artifacts. Therefore, conventional audio dynamics processes can often introduce audible artifacts, i.e., the effects of the dynamics processing can introduce unwanted perceptible changes in the audio.
DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart showing an example of processing steps for performing auditory scene analysis.
BEST MODE FOR CARRYING OUT THE INVENTION Auditory Scene Analysis Original, Non-Loudness Domain Method In accordance with an embodiment of one aspect of the present invention, auditory scene analysis may be composed of four general processing steps as shown in a portion of FIG. 1. The first step 1-1 (�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 may 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. 1 calculates the spectral content of successive time segments of the audio signal. In a practical embodiment, the ASA block size may be from any number of samples of the input audio signal, although 512 samples provide a good tradeoff of time and frequency resolution. In the second step 1-2, 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 1-3 (�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.
Step 1-3 identifies the locations of auditory event boundaries by applying a threshold to the array of difference measures from step 1-2 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 1-1) 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.
The process of FIG. 1 may be represented more generally by the equivalent arrangements of FIGS. 3, 4 and 5. In FIG. 3, an audio signal is applied in parallel to an �Identify Auditory Events� function or step 3-1 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 3-2. The process of FIG. 1 may be employed to divide the audio signal into auditory events and their characteristics identified 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 3-1 is then used to modify the audio dynamics processing parameters (such as attack, release, ratio, etc.), as desired, by a �Modify Dynamics Parameters� function or step 3-3. The optional �Identify Characteristics� function or step 3-3 also receives the auditory event information. The �Identify Characteristics� function or step 3-3 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. 1. The characteristics may also include one or more audio characteristics, 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, or other characteristics that help modify dynamics parameters such that negative audible artifacts of the processing are reduced or removed. The characteristics may also include other characteristics such as whether the auditory event includes a transient.
Alternatives to the arrangement of FIG. 3 are shown in FIGS. 4 and 5. In FIG. 4, the audio input signal is not applied directly to the �Identify Characteristics� function or step 4-3, but it does receive information from the �Identify Auditory Events� function or step 4-1. The arrangement of FIG. 1 is a specific example of such an arrangement. In FIG. 5, the functions or steps 5-1, 5-2 and 5-3 are arranged in series.
Auditory Scene Analysis New, Loudness Domain Method International application under the Patent Cooperation Treaty S.N. PCT/US2005/038579, filed Oct. 25, 2005, published as International Publication Number WO 2006/047600 A1, entitled �Calculating and Adjusting the Perceived Loudness and/or the Perceived Spectral Balance of an Audio Signal� by Alan Jeffrey Seefeldt discloses, among other things, an objective measure of perceived loudness based on a psychoacoustic model. Said application is hereby incorporated by reference in its entirety. As described in said application, from an audio signal, x[n], an excitation signal E[b,t] is computed that approximates the distribution of energy along the basilar membrane of the inner ear at critical band b during time block t. This excitation may be computed from the Short-time Discrete Fourier Transform (STDFT) of the audio signal as follows:
E ⁡ [ b , t ] = λ b ⁢ E ⁡ [ b , t - 1 ] + ( 1 - λ b ) ⁢ ∑ k ⁢  T ⁡ [ k ]  2 ⁢  C b ⁡ [ k ]  2 ⁢  X ⁡ [ k , t ]  2 ( 1 ) where X[k,t] represents the STDFT of x[n] at time block t and bin k. Note that in equation 1 t represents time in discrete units of transform blocks as opposed to a continuous measure, such as seconds. T[k] represents the frequency response of a filter simulating the transmission of audio through the outer and middle ear, and Cb[k] represents the frequency response of the basilar membrane at a location corresponding to critical band b. FIG. 6 depicts a suitable set of critical band filter responses in which 40 bands are spaced uniformly along the Equivalent Rectangular Bandwidth (ERB) scale, as defined by Moore and Glasberg. Each filter shape is described by a rounded exponential function and the bands are distributed using a spacing of 1 ERB. Lastly, the smoothing time constant λb in equation 1 may be advantageously chosen proportionate to the integration time of human loudness perception within band b.
N ⁡ [ b , t ] = β ⁡ ( ( E 1 ⁢ ⁢ kHz ⁡ [ b , t ] TQ 1 ⁢ ⁢ kHz ) a - 1 ) ( 2 ) where TQ1kHz is the threshold in quiet at 1 kHz and the constants β and α are chosen to match growth of loudness data as collected from listening experiments. Abstractly, this transformation from excitation to specific loudness may be presented by the function Ψ{ } such that:
N[b,t]=Ψ{E[b,t]} Finally, the total loudness, L[t], represented in units of sone, is computed by summing the specific loudness across bands:
L ⁡ [ t ] = ∑ b ⁢ N ⁡ [ b , t ] ( 3 ) The specific loudness N[b,t] is a spectral representation meant to simulate the manner in which a human perceives audio as a function of frequency and time. It captures variations in sensitivity to different frequencies, variations in sensitivity to level, and variations in frequency resolution. As such, it is a spectral representation well matched to the detection of auditory events. Though more computationally complex, comparing the difference of N[b,t] across bands between successive time blocks may in many cases result in more perceptually accurate detection of auditory events in comparison to the direct use of successive FFT spectra described above.
Audio Dynamics Processing Parameter Control with Auditory Events Two examples of embodiments of the invention are now presented. The first describes the use of auditory events to control the release time in a digital implementation of a Dynamic Range Controller (DRC) in which the gain control is derived from the Root Mean Square (RMS) power of the signal. The second embodiment describes the use of auditory events to control certain aspects of a more sophisticated combination of AGC and DRC implemented within the context of the psychoacoustic loudness model described above. These two embodiments are meant to serve as examples of the invention only, and it should be understood that the use of auditory events to control parameters of a dynamics processing algorithm is not restricted to the specifics described below.
Dynamic Range Control The described digital implementation of a DRC segments an audio signal x[n] into windowed, half-overlapping blocks, and for each block a modification gain based on a measure of the signal's local power and a selected compression curve is computed. The gain is smoothed across blocks and then multiplied with each block. The modified blocks are finally overlap-added to generate the modified audio signal y[n].
P ⁡ [ t ] = 10 * log ⁢ ⁢ 10 ⁢ ( 1 M ⁢ ∑ n = 1 M ⁢ x 2 ⁡ [ n , t ] ) . ( 5 ) As mentioned earlier, one could smooth this power measure with a fast attack and slow release prior to processing with a compression curve, but as an alternative the instantaneous power P[t] is processed and the resulting gain is smoothed. This alternate approach has the advantage that a simple compression curve with sharp knee points may be used, but the resulting gains are still smooth as the power travels through the knee-point. Representing a compression curve as shown in FIG. 8 c as a function F of signal level that generates a gain, the block gain G[t] is given by:
Assuming that the compression curve applies greater attenuation as signal level increases, the gain will be decreasing when the signal is in �attack mode� and increasing when in �release mode�. Therefore, a smoothed gain G[t] may be computed according to:
G _ ⁡ [ t ] = α ⁡ [ t ] � G _ ⁡ [ t - 1 ] + ( 1 - α ⁡ [ t ] ) ⁢ G ⁡ [ t ] ⁢ ⁢ where ( 7 ⁢ a ) α ⁡ [ t ] = { α attach G ⁡ [ t ] < G _ ⁡ [ t - 1 ] α release G ⁡ [ t ] ≥ G _ ⁡ [ t - 1 ] . ⁢ ⁢ and ( 7 ⁢ b ) α release >> α attach ( 7 ⁢ c ) Finally, the smoothed gain G[t], which is in dB, is applied to each block of the signal, and the modified blocks are overlap-added to produce the modified audio:
A suitable behavior of the release control is now described. In qualitative terms, if an event is detected, the gain is smoothed with the release time constant as specified above in Equation 7a. As time evolves past the detected event, and if no subsequent events are detected, the release time constant continually increases so that eventually the smoothed gain is �frozen� in place. If another event is detected, then the smoothing time constant is reset to the original value and the process repeats. In order to modulate the release time, one may first generate a control signal based on the detected event boundaries.
X ⁡ [ k , t ] = ∑ n = 0 M - 1 ⁢ x ⁡ [ n , t ] ⁢ ⅇ - j ⁢ 2 ⁢ π ⁢ ⁢ kn M ( 9 ) Next, the difference between the normalized log magnitude spectra of successive blocks may be computed according to:
D ⁡ [ t ] = ∑ k ⁢  X NORM ⁡ [ k , t ] - X NORM ⁡ [ k , t - 1 ]  ⁢ ⁢ where ( 10 ⁢ a ) X NORM ⁡ [ k , t ] = log ⁡ (  X ⁡ [ k , t ]  max k ⁢ {  X ⁡ [ k , t ]  } ) ( 10 ⁢ b ) Here the maximum of |X[k,t]| across bins k is used for normalization, although one might employ other normalization factors; for example, the average of |X[k,t]| across bins. If the difference D[t] exceeds a threshold Dmin, then an event is considered to have occurred. Additionally, one may assign a strength to this event, lying between zero and one, based on the size of D[t] in comparison to a maximum threshold Dmax. The resulting auditory event strength signal A[t] may be computed as:
A ⁡ [ t ] = { ⁢ 0 ⁢ D ⁡ [ t ] ≤ D min D ⁡ [ t ] - D mi ⁢ n D max - D min D min < D ⁡ [ t ] < D max ⁢ 1 ⁢ D ⁡ [ t ] ≥ D max ( 11 ) By assigning a strength to the auditory event proportional to the amount of spectral change associated with that event, greater control over the dynamics processing is achieved in comparison to a binary event decision. The inventors have found that larger gain changes are acceptable during stronger events, and the signal in equation 11 allows such variable control.
A _ ⁡ [ t ] = { ⁢ A ⁡ [ t ] ⁢ A ⁡ [ t ] > α event ⁢ A _ ⁡ [ t - 1 ] α event ⁢ A _ ⁡ [ t - 1 ] ⁢ otherwise ( 12 ) Here αevent controls the decay time of the event control signal. FIGS. 9 d and 10 d depict the event control signal Ā[t] for the two corresponding audio signals, with the half-decay time of the smoother set to 250 ms. In the first case, one sees that an event boundary is detected for each of the six piano chords, and that the event control signal decays smoothly towards zero after each event. For the second signal, many events are detected very close to each other in time, and therefore the event control signal never decays fully to zero.
α ⁡ [ t ] = { ⁢ α attack G ⁡ [ t ] < G _ ⁡ [ t - 1 ] ⁢ A _ ⁡ [ t ] ⁢ α release + ( 1 - A _ ⁡ [ t ] ) G ⁡ [ t ] ≥ G _ ⁡ [ t - 1 ] ( 13 ) By interpolating the smoothing coefficient continuously as a function of the event control signal, the release time is reset to a value proportionate to the event strength at the onset of an event and then increases smoothly to infinity after the occurrence of an event. The rate of this increase is dictated by the coefficient αevent used to generate the smoothed event control signal.
Loudness Based AGC and DRC As an alternative to traditional dynamics processing techniques where signal modifications are a direct function of simple signal measurements such as Peak or RMS power, International Patent Application S.N. PCT/US2005/038579 discloses use of the psychoacoustic based loudness model described earlier as a framework within which to perform dynamics processing. Several advantages are cited. First, measurements and modifications are specified in units of sone, which is a more accurate measure of loudness perception than more basic measures such as Peak or RMS power. Secondly, the audio may be modified such that the perceived spectral balance of the original audio is maintained as the overall loudness is changed. This way, changes to the overall loudness become less perceptually apparent in comparison to a dynamics processor that utilizes a wideband gain, for example, to modify the audio. Lastly, the psychoacoustic model is inherently multi-band, and therefore the system is easily configured to perform multi-band dynamics processing in order to alleviate the well-known cross-spectral pumping problems associated with a wideband dynamics processor.
The loudness domain dynamics processing system that is now described consists of AGC followed by DRC. The goal of this combination is to make all processed audio have approximately the same perceived loudness while still maintaining at least some of the original audio's dynamics. FIG. 11 depicts a suitable set of AGC and DRC curves for this application. Note that the input and output of both curves is represented in units of sone since processing is performed in the loudness domain. The AGC curve strives to bring the output audio closer to some target level, and, as mentioned earlier, does so with relatively slow time constants. One may think of the AGC as making the long-term loudness of the audio equal to the target, but on a short-term basis, the loudness may fluctuate significantly around this target. Therefore, one may employ faster acting DRC to limit these fluctuations to some range deemed acceptable for the particular application. FIG. 11 shows such a DRC curve where the AGC target falls within the �null band� of the DRC, the portion of the curve that calls for no modification. With this combination of curves, the AGC places the long-term loudness of the audio within the null-band of the DRC curve so that minimal fast-acting DRC modifications need be applied. If the short-term loudness still fluctuates outside of the null-band, the DRC then acts to move the loudness of the audio towards this null-band. As a final general note, one may apply the slow acting AGC such that all bands of the loudness model receive the same amount of loudness modification, thereby maintaining the perceived spectral balance, and one may apply the fast acting DRC in a manner that allows the loudness modification to vary across bands in order alleviate cross-spectral pumping that might otherwise result from fast acting band-independent loudness modification.
D ⁡ [ t ] = ∑ b ⁢  N NORM ⁡ [ b , t ] - N NORM ⁡ [ b , t - 1 ]  ⁢ ⁢ where ( 14 ⁢ a ) N NORM ⁡ [ b , t ] = N ⁡ [ b , t ] max b ⁢ { N ⁡ [ b , t ] } ( 14 ⁢ b ) Here the maximum of |N[b,t]| across frequency bands b is used for normalization, although one might employ other normalization factors; for example, the average of |N[b,t]| across frequency bands. If the difference D[t] exceeds a threshold Dmin, then an event is considered to have occurred. The difference signal may then be processed in the same way shown in Equations 11 and 12 to generate a smooth event control signal Ā[t] used to control the attack and release times.
L AGC ⁡ [ t ] = α AGC ⁡ [ t ] ⁢ L AGC ⁡ [ t - 1 ] + ( 1 - α AGC ⁡ [ t ] ) ⁢ L ⁡ [ t ] ⁢ ⁢ where ( 16 ⁢ a ) α AGC ⁡ [ t ] = { A _ ⁡ [ t ] ⁢ α AGCattach + ( 1 - A _ ⁡ [ t ] ) L ⁡ [ t ] > L AGC ⁡ [ t - 1 ] A _ ⁡ [ t ] ⁢ α AGCrelease + ( 1 - A _ ⁡ [ t ] ) L ⁡ [ t ] ≤ L AGC ⁡ [ t - 1 ] . ( 16 ⁢ b ) In addition, one may compute an associated long-term specific loudness spectrum that will later be used for the multi-band DRC:
S AGC ⁡ [ t ] = F AGC ⁢ { L AGC ⁡ [ t ] } L AGC ⁡ [ t ] ( 17 ) The DRC modification may now be computed from the loudness after the application of the AGC scaling. Rather than smooth a measure of the loudness prior to the application of the DRC curve, one may alternatively apply the DRC curve to the instantaneous loudness and then subsequently smooth the resulting modification. This is similar to the technique described earlier for smoothing the gain of the traditional DRC. In addition, the DRC may be applied in a multi-band fashion, meaning that the DRC modification is a function of the specific loudness N[b,t] in each band b, rather than the overall loudness L[t]. However, in order to maintain the average spectral balance of the original audio, one may apply DRC to each band such that the resulting modifications have the same average effect as would result from applying DRC to the overall loudness. This may be achieved by scaling each band by the ratio of the long-term overall loudness (after the application of the AGC scaling) to the long-term specific loudness, and using this value as the argument to the DRC function. The result is then rescaled by the inverse of said ratio to produce the output specific loudness. Thus, the DRC scaling in each band may be computed according to:
S DRC ⁡ [ b , t ] = N AGC ⁡ [ b , t ] S AGC ⁡ [ t ] ⁢ L AGC ⁡ [ t ] ⁢ F DRC ⁢ { S AGC ⁡ [ t ] ⁢ L AGC ⁡ [ t ] N AGC ⁡ [ t ] ⁢ N ⁡ [ b , t ] } N ⁡ [ b , t ] ( 18 ) The AGC and DRC modifications may then be combined to form a total loudness scaling per band:
S _ TOT ⁡ [ b , t ] = exp ⁡ ( α TOT ⁡ [ b , t ] ⁢ log ⁡ ( S _ TOT ⁡ [ b , t - 1 ] ) + ( 1 - α TOT ⁡ [ b , t ] ) ⁢ log ⁡ ( S TOT ⁡ [ b , t ] ) ) ( 20 ⁢ a ) N _ ⁡ [ b , t ] = α TOT ⁡ [ b , t ] ⁢ N _ ⁡ [ b , t - 1 ] + ( 1 - α TOT ⁡ [ b , t ] ) ⁢ N ⁡ [ b , t ] ⁢ ⁢ where ( 20 ⁢ b ) α TOT ⁡ [ b , t ] = { ⁢ α TOTattack N ⁡ [ b , t ] > N _ ⁡ [ b , t - 1 ] ⁢ A _ ⁡ [ t ] ⁢ α TOTrelease + ( 1 - A _ ⁡ [ t ] ) N ⁡ [ b , t ] ≤ N _ ⁡ [ b , t - 1 ] ( 20 ⁢ c ) Finally one may compute a target specific loudness based on the smoothed scaling applied to the original specific loudness
Additional Parameter Control While the discussion above has focused on the control of AGC and DRC attack and release parameters via auditory scene analysis of the audio being processed, other important parameters may also benefit from being controlled via the ASA results. For example, the event control signal Ā[t] from Equation 12 may be used to vary the value of the DRC ratio parameter that is used to dynamically adjust the gain of the audio. The Ratio parameter, similarly to the attack and release time parameters, may contribute significantly to the perceptual artifacts introduced by dynamic gain adjustments.
Audio Dynamics Processing Audio Engineer's Reference Book, edited by Michael Talbot-Smith, 2nd edition. Limiters and Compressors, Alan Tutton, 2-1492-165. Focal Press, Reed Educational and Professional Publishing, Ltd., 1999.
Detecting and Using Auditory Events U.S. patent application Ser. No. 10/474,387, �High Quality Time-Scaling and Pitch-Scaling of Audio Signals� of Brett Graham Crockett, published Jun. 24, 2004 as US 2004/0122662 A1.
U.S. patent application Ser. No. 10/478,398, �Method for Time Aligning Audio Signals Using Characterizations Based on Auditory Events� of Brett G. Crockett et al, published Jul. 29, 2004 as US 2004/0148159 A1.
U.S. patent application Ser. No. 10/478,538, �Segmenting Audio Signals Into Auditory Events� of Brett G. Crockett, published Aug. 26, 2004 as US 2004/0165730 A1. Aspects of the present invention provide a way to detect auditory events in addition to those disclosed in said application of Crockett.
U.S. patent application Ser. No. 10/478,397, �Comparing Audio Using Characterizations Based on Auditory Events� of Brett G. Crockett et al, published Sep. 2, 2004 as US 2004/0172240 A1.
International Application under the Patent Cooperation Treaty S.N. PCT/US 05/24630 filed Jul. 13, 2005, entitled �Method for Combining Audio Signals Using Auditory Scene Analysis,� of Michael John Smithers, published Mar. 9, 2006 as WO 2006/026161.
International Application under the Patent Cooperation Treaty S.N. PCT/US 2004/016964, filed May 27, 2004, entitled �Method, Apparatus and Computer Program for Calculating and Adjusting the Perceived Loudness of an Audio Signal� of Alan Jeffrey Seefeldt et al, published Dec. 23, 2004 as WO 2004/111994 A2.
International application under the Patent Cooperation Treaty S.N. PCT/US2005/038579, filed Oct. 25, 2005, entitled �Calculating and Adjusting the Perceived Loudness and/or the Perceived Spectral Balance of an Audio Signal� by Alan Jeffrey Seefeldt and published as International Publication Number WO 2006/047600.
�A Method for Characterizing and Identifying Audio Based on Auditory Scene Analysis,� by Brett Crockett and Michael Smithers, Audio Engineering Society Convention Paper 6416, 118th Convention, Barcelona, May 28-31, 2005.
�High Quality Multichannel Time Scaling and Pitch-Shifting using Auditory Scene Analysis,� by Brett Crockett, Audio Engineering Society Convention Paper 5948, New York, October 2003.
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