Abstract: A device includes a memory and one or more processors coupled to the memory and configured to execute instructions from the memory. Execution of the instructions causes the one or more processors to combine two or more data portions to generate input data for a decoder network. A first data portion of the two or more data portions is based on a first encoding of a data sample by a multiple description coding network and content of a second data portion of the two or more data portions depends on whether data based on a second encoding of the data sample by the multiple description coding network is available. Execution of the instructions also causes the one or more processors to obtain, from the decoder network, output data based on the input data and to generate a representation of the data sample based on the output data.

Abstract: A device includes a memory configured to store instructions and a processor coupled to the memory. The processor includes a first processing unit configured to perform a first stage of a sample synthesis operation. The processor includes a second processing unit configured to perform a second stage of the sample synthesis operation based on an output of the first processing unit. The processor also includes a sample synthesizer configured to process input data, using the first processing unit and the second processing unit, to generate output data. The first processing unit and the second processing unit are configured to operate in a pipelined configuration that includes performance of the second stage at the second processing unit in parallel with performance of the first stage at the first processing unit.

Abstract: A device includes a neural network and a sample generator. The neural network is configured to process one or more neural network inputs to generate a joint probability distribution. The one or more neural network inputs include at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples. The sample generator is configured to generate first sample data and second sample data based on the joint probability distribution. The first sample data and the second sample data are associated with at least one data sample of the sequence of data samples.

Abstract: Systems and techniques are described for coding audio signals. For example, a voice decoder can generate, using a first neural network, an excitation signal for at least one sample of an audio signal at least in part by performing a non-linear operation based on one or more inputs to the first neural network, the excitation signal being configured to excite a learned linear filter. The voice decoder can further generate, using the learned linear filter and the excitation signal, at least one sample of a reconstructed audio signal. For example, a second neural network can be used to generate coefficients for one or more learned linear filters, which receive as input the excitation signal generated by the first neural network trained to perform the non-linear operation.

Abstract: Systems and techniques are described for coding audio signals. For example, a voice decoder can generate, using a neural network, an excitation signal for at least one sample of an audio signal based on one or more inputs to the neural network, the excitation signal being configured to excite a linear predictive coding (LPC) filter. The voice decoder can further generate, using the LPC filter based on the excitation signal, at least one sample of a reconstructed audio signal. For example, the neural network can generate coefficients for one or more linear time-varying filters (e.g., a linear time-varying harmonic filter and a linear time-varying noise filter). The voice decoder can use the one or more linear time-varying filters including the generated coefficients to generate the excitation signal.

Abstract: Systems and techniques are described for audio coding. An audio system receives feature(s) corresponding an audio signal, for example from an encoder and/or a speech synthesis engine. The audio system generates an excitation signal, such as a harmonic signal and/or a noise signal, based on the feature(s). The audio system uses a filterbank to generate band-specific signals from the excitation signal. The band-specific signals correspond to frequency bands. The audio system inputs the feature(s) into a machine learning (ML) filter estimator to generate parameter(s) associated with linear filter(s). The audio system inputs the feature(s) into a voicing estimator to generate gain value(s). The audio system generates an output audio signal based on modification of the band-specific signals, application of the linear filter(s) according to the parameter(s), and amplification using the gain amplifier(s) according to the gain value(s).

Abstract: A method includes receiving audio data that includes magnitude spectrum data descriptive of an audio signal. The method also includes providing the audio data as input to a neural network to generate an initial phase estimate for one or more samples of the audio signal. The method further includes determining, using a phase estimation algorithm, target phase data for the one or more samples of the audio signal based on the initial phase estimate and a magnitude spectrum of the one or more samples of the audio signal indicated by the magnitude spectrum data. The method also includes reconstructing the audio signal based on a target phase of the one or more samples of the audio signal indicated by the target phase data and based on the magnitude spectrum.

Abstract: Techniques are described for coding audio signals. For example, using a neural network, a residual signal is generated for a sample of an audio signal based on inputs to the neural network. The residual signal is configured to excite a long-term prediction filter and/or a short-term prediction filter. Using the long-term prediction filter and/or the short-term prediction filter, a sample of a reconstructed audio signal is determined. The sample of the reconstructed audio signal is determined based on the residual signal generated using the neural network for the sample of the audio signal.

Abstract: Techniques are described for coding audio signals. For example, using a neural network, a residual signal is generated for a sample of an audio signal based on inputs to the neural network. The residual signal is configured to excite a long-term prediction filter and/or a short-term prediction filter. Using the long-term prediction filter and/or the short-term prediction filter, a sample of a reconstructed audio signal is determined. The sample of the reconstructed audio signal is determined based on the residual signal generated using the neural network for the sample of the audio signal.