The present disclosure discloses an inter-channel phase difference parameter encoding method, where a current frame is obtained; a signal type and a previous IPD parameter encoding scheme of a previous frame are obtained; a current IPD parameter encoding scheme is obtained at least based on the signal type of the previous frame and the previous IPD parameter encoding scheme; and an IPD parameter of the current frame is processed based on the current IPD parameter encoding scheme.

The present disclosure discloses an inter-channel phase difference parameter encoding method, where a current frame is obtained; a signal type and a previous IPD parameter encoding scheme of a previous frame are obtained; a current IPD parameter encoding scheme is obtained at least based on the signal type of the previous frame and the previous IPD parameter encoding scheme; and an IPD parameter of the current frame is processed based on the current IPD parameter encoding scheme.

A method for generation of comfort noise for at least two audio channels. The method comprises determining a spatial coherence between audio signals on the respective audio channels, wherein at least one spatial coherence value per frame and frequency band is determined to form a vector of spatial coherence values. A vector of predicted spatial coherence values is formed by a weighted combination of a first coherence prediction and a second coherence prediction that are combined using a weight factor α. The method comprises signaling information about the weight factor α to the receiving node, for enabling the generation of the comfort noise for the at least two audio channels at the receiving node.

A method for generation of comfort noise for at least two audio channels. The method comprises determining a spatial coherence between audio signals on the respective audio channels, wherein at least one spatial coherence value per frame and frequency band is determined to form a vector of spatial coherence values. A vector of predicted spatial coherence values is formed by a weighted combination of a first coherence prediction and a second coherence prediction that are combined using a weight factor α. The method comprises signaling information about the weight factor α to the receiving node, for enabling the generation of the comfort noise for the at least two audio channels at the receiving node.

Provided is an audio transcoding method, including: (301) performing entropy decoding on a first audio stream with a first bitrate, to obtain an audio feature parameter and an excitation signal of the first audio stream, the excitation signal being a quantized audio signal; (302) obtaining a time-domain audio signal corresponding to the excitation signal based on the audio feature parameter and the excitation signal; (303) re-quantizing the excitation signal and the audio feature parameter based on the time-domain audio signal and a target transcoding bitrate, to obtain a target excitation signal and a target audio feature parameter; and (304) performing entropy coding on the target audio feature parameter and the target excitation signal, to obtain a second audio stream with a second bitrate, the second bitrate being lower than the first bitrate.

Provided is an audio transcoding method, including: (301) performing entropy decoding on a first audio stream with a first bitrate, to obtain an audio feature parameter and an excitation signal of the first audio stream, the excitation signal being a quantized audio signal; (302) obtaining a time-domain audio signal corresponding to the excitation signal based on the audio feature parameter and the excitation signal; (303) re-quantizing the excitation signal and the audio feature parameter based on the time-domain audio signal and a target transcoding bitrate, to obtain a target excitation signal and a target audio feature parameter; and (304) performing entropy coding on the target audio feature parameter and the target excitation signal, to obtain a second audio stream with a second bitrate, the second bitrate being lower than the first bitrate.

Various embodiments provide a method and an apparatus for determining a weighting factor during stereo signal encoding. In those embodiments, a parameter value corresponding to the encoding mode of the to-be-encoded signal is determined based on an encoding mode of a to-be-encoded signal in a stereo signal and a correspondence between an encoding mode and a parameter value. Based on the determined parameter value and an energy spectrum of a linear prediction filter corresponding to an original line spectral frequency parameter of the to-be-encoded signal, a weighting factor for calculating a distance between the original line spectral frequency parameter and a target original line spectral frequency parameter is calculated.

Various embodiments provide a method and an apparatus for determining a weighting factor during stereo signal encoding. In those embodiments, a parameter value corresponding to the encoding mode of the to-be-encoded signal is determined based on an encoding mode of a to-be-encoded signal in a stereo signal and a correspondence between an encoding mode and a parameter value. Based on the determined parameter value and an energy spectrum of a linear prediction filter corresponding to an original line spectral frequency parameter of the to-be-encoded signal, a weighting factor for calculating a distance between the original line spectral frequency parameter and a target original line spectral frequency parameter is calculated.

Methods of encoding and decoding a speech signal using a neural network model that recognizes sound sources, and encoding and decoding apparatuses for performing the methods are provided. A method of encoding a speech signal includes identifying an input signal for a plurality of sound sources; generating a latent signal by encoding the input signal; obtaining a plurality of sound source signals by separating the latent signal for each of the plurality of sound sources; determining a number of bits used for quantization of each of the plurality of sound source signals according to a type of each of the plurality of sound sources; quantizing each of the plurality of sound source signals based on the determined number of bits; and generating a bitstream by combining the plurality of quantized sound source signals.

Methods of encoding and decoding a speech signal using a neural network model that recognizes sound sources, and encoding and decoding apparatuses for performing the methods are provided. A method of encoding a speech signal includes identifying an input signal for a plurality of sound sources; generating a latent signal by encoding the input signal; obtaining a plurality of sound source signals by separating the latent signal for each of the plurality of sound sources; determining a number of bits used for quantization of each of the plurality of sound source signals according to a type of each of the plurality of sound sources; quantizing each of the plurality of sound source signals based on the determined number of bits; and generating a bitstream by combining the plurality of quantized sound source signals.