Abstract: Some audio processing methods may involve receiving audio data corresponding to a plurality of audio channels and determining audio characteristics of the audio data, which may include transient information. An amount of decorrelation for the audio data may be based, at least in part, on the audio characteristics. If a definite transient event is determined, a decorrelation process may be temporarily halted or slowed. Determining transient information may involve evaluating the likelihood and/or the severity of a transient event. In some implementations, determining transient information may involve evaluating a temporal power variation in the audio data. Explicit transient information may or may not be received with the audio data, depending on the implementation. Explicit transient information may include a transient control value corresponding to a definite transient event, a definite non-transient event or an intermediate transient control value.

Abstract: Received audio data may include a first set of frequency coefficients and a second set of frequency coefficients. Spatial parameters for at least part of the second set of frequency coefficients may be estimated, based at least in part on the first set of frequency coefficients. The estimated spatial parameters may be applied to the second set of frequency coefficients to generate a modified second set of frequency coefficients. The first set of frequency coefficients may correspond to a first frequency range (for example, an individual channel frequency range) and the second set of frequency coefficients may correspond to a second frequency range (for example, a coupled channel frequency range). Combined frequency coefficients of a composite coupling channel may be based on frequency coefficients of two or more channels. Cross-correlation coefficients, between frequency coefficients of a first channel and the combined frequency coefficients, may be computed.

Abstract: Audio processing methods may involve receiving audio data corresponding to a plurality of audio channels. The audio data may include a frequency domain representation corresponding to filterbank coefficients of an audio encoding or processing system. A decorrelation process may be performed with the same filterbank coefficients used by the audio encoding or processing system. The decorrelation process may be performed without converting coefficients of the frequency domain representation to another frequency domain or time domain representation. The decorrelation process may involve selective or signal-adaptive decorrelation of specific channels and/or specific frequency bands. The decorrelation process may involve applying a decorrelation filter to a portion of the received audio data to produce filtered audio data. The decorrelation process may involve using a non-hierarchal mixer to combine a direct portion of the received audio data with the filtered audio data according to spatial parameters.

Abstract: Decorrelation filter parameters for audio data may be based, at least in part, on audio characteristics such as tonality information and/or transient information. Determining the audio characteristics may involve receiving explicit audio characteristics with the audio data and/or determining audio characteristics based on one or more attributes of the audio data. The decorrelation filter parameters may include dithering parameters and/or randomly selected pole locations for at least one pole of an all-pass filter. The dithering parameters and/or pole locations may involve a maximum stride value for pole movement. In some examples, the maximum stride value may be substantially zero for highly tonal signals of the audio data. The dithering parameters and/or pole locations may be bounded by constraint areas within which pole movements are constrained. The constraint areas may or may not be fixed. In some implementations, different channels of the audio data may share the same constraint areas.

Abstract: On the basis of a bitstream (P), an n-channel audio signal (X) is reconstructed by deriving an m-channel core signal (Y) and multichannel coding parameters (?) from the bitstream, where 1?m<n. Also derived from the bitstream are pre-processing dynamic range control, DRC, parameters (DRC2) quantifying an encoder-side dynamic range limiting of the core signal. The n-channel audio signal is obtained by parametric synthesis in accordance with the multichannel coding parameters and while cancelling any encoder-side dynamic range limiting based on the pre-processing DRC parameters. In particular embodiments, the reconstruction further includes use of compensated post-processing DRC parameters quantifying a potential decoder-side dynamic range compression. Cancellation of an encoder-side range limitation and range compression are preferably performed by different decoder-side components. Cancellation and compression may be coordinated by a DRC pre-processor.

Abstract: A method for determining mantissa bit allocation of audio data values of frequency domain audio data to be encoded. The allocation method includes a step of determining masking values for the audio data values, including by performing adaptive low frequency compensation on the audio data of each frequency band of a set of low frequency bands of the audio data. The adaptive low frequency compensation includes steps of: performing tonality detection on the audio data to generate compensation control data indicative of whether each frequency band in the set of low frequency bands has prominent tonal content; and performing low frequency compensation on the audio data in each frequency band in the set of low frequency bands having prominent tonal content as indicated by the compensation control data, but not performing low frequency compensation on the audio data in any other frequency band in the set of low frequency bands.

Abstract: A method and apparatus provide the ability to resample video frame into various resolutions, and to predict, quantize, and entropy code the video signal for spatially scalable compression and networking applications. The solution involves a unified re-sampling and estimation-theoretic prediction, quantization, and entropy coding framework, which by design is tailored to allow base layer coding information to be fully accessible and usable by enhancement layers; and for the enhancement layer to account for all available information from both layers for superior compression performance. Specialization may include specific techniques for coding and networking scenarios, where the potential of the unified resampling and estimation-theoretic framework is realized to considerably improve the overall system performance over existing schemes.

Abstract: A method for determining mantissa bit allocation of frequency domain audio data to be encoded, including by performing adaptive low frequency compensation on each frequency band of a set of low frequency bands of the data.

Abstract: A method for determining mantissa bit allocation of frequency domain audio data to be encoded, including by performing adaptive low frequency compensation on each frequency band of a set of low frequency bands of the data. The low frequency compensation includes steps of: performing tonality detection on the audio data to generate compensation control data indicative of whether each frequency band in the set has prominent tonal content; and performing low frequency compensation on each frequency band in the set having prominent tonal content, including by correcting a preliminary masking value for each frequency band having prominent tonal content, but not performing low frequency compensation on the audio data in any other frequency band in the set. Other aspects are audio encoding methods including such tonality detection and low frequency compensation steps, and a system configured to perform any embodiment of the inventive method.

Abstract: Some methods may involve receiving a frame of encoded audio data that includes transform coefficient data. The transform coefficient data may include exponent data and mantissa data. The mantissa data may include mantissa values that were encoded with uniform or non-uniform boundaries of quantization intervals. The mantissa values may be reconstructed based, at least in part, on exponent profile data. Based on the exponent profile data, statistics regarding the pre-quantization mantissas values may be inferred. The exponent profile data may include exponent differential data. Some such exponent differential data may be exponent difference pairs, though more than two exponent differential data points may be evaluated in alternative methods. At each frequency bin, mantissa value reconstruction may be conditioned on the exponent differential data, e.g., on the exponent difference pairs.