Abstract:
Audio characteristics of audio data corresponding to a plurality of audio channels may be determined. The audio characteristics may include spatial parameter data. Decorrelation filtering processes for the audio data may be based, at least in part, on the audio characteristics. The decorrelation filtering processes may cause a specific inter-decorrelation signal coherence (“IDC”) between channel-specific decorrelation signals for at least one pair of channels. The channel-specific decorrelation signals may be received and/or determined. Inter-channel coherence (“ICC”) between a plurality of audio channel pairs may be controlled. Controlling ICC may involve at receiving an ICC value and/or determining an ICC value based, at least partially, on the spatial parameter data. A set of IDC values may be based, at least partially, on the set of ICC values. A set of channel-specific decorrelation signals, corresponding with the set of IDC values, may be synthesized by performing operations on the filtered audio data.
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:
Computer-implemented methods for training a neural network, as well as for implementing audio encoders and decoders via trained neural networks, are provided. The neural network may receive an input audio signal, generate an encoded audio signal and decode the encoded audio signal. A loss function generating module may receive the decoded audio signal and a ground truth audio signal, and may generate a loss function value corresponding to the decoded audio signal. Generating the loss function value may involve applying a psychoacoustic model. The neural network may be trained based on the loss function value. The training may involve updating at least one weight of the neural network.
Abstract:
The present invention relates to a method for predicting transform coefficients representing frequency content of an adaptive block length media signal, by receiving a frame and receiving block length information indicating a number of quantized transform coefficients for each block in the frame, the number of quantized transform coefficients being one of a first or second number, wherein the first number is greater than the second number, determining a first block has the second number of quantized transform coefficients, converting the first block into a converted block having the first number of quantized transform coefficients, conditioning a main neural network trained to predict at least one output variable given at least one conditioning variable, the at least one conditioning variable being based on information regarding the converted block and block length information for the first block, providing at least one predicted transform coefficients from an output stage of the main neural network.
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:
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:
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:
In some embodiments, virtualization methods for generating a binaural signal in response to channels of a multi-channel audio signal, which apply a binaural room impulse response (BRIR) to each channel including by using at least one feedback delay network (FDN) to apply a common late reverberation to a downmix of the channels. In some embodiments, input signal channels are processed in a first processing path to apply to each channel a direct response and early reflection portion of a single-channel BRIR for the channel, and the downmix of the channels is processed in a second processing path including at least one FDN which applies the common late reverberation. Typically, the common late reverberation emulates collective macro attributes of late reverberation portions of at least some of the single-channel BRIRs. Other aspects are headphone virtualizers configured to perform any embodiment of the method.
Abstract:
Methods and systems for designing binaural room impulse responses (BRIRs) for use in headphone virtualizers, and methods and systems for generating a binaural signal in response to a set of channels of a multi-channel audio signal, including by applying a BRIR to each channel of the set, thereby generating filtered signals, and combining the filtered signals to generate the binaural signal, where each BRIR has been designed in accordance with an embodiment of the design method. Other aspects are audio processing units configured to perform any embodiment of the inventive method. In accordance with some embodiments, BRIR design is formulated as a numerical optimization problem based on a simulation model (which generates candidate BRIRs) and at least one objective function (which evaluates each candidate BRIR), and includes identification of a best one of the candidate BRIRs as indicated by performance metrics determined for the candidate BRIRs by each objective function.
Abstract:
Described herein is a method of processing an audio signal using a neural network or using a first and a second neural network. Described is further a method of training said neural network or of jointly training a set of said first and said second neural network. Moreover, described is a method of obtaining and transmitting a latent feature space representation of a perceptual domain audio signal using a neural network and a method of obtaining an audio signal from a latent feature space representation of a perceptual domain audio signal using a neural network. Described are also respective apparatuses and computer program products.