Neural Audio Codec
A neural audio codec is a neural network-based audio compression and reconstruction system that encodes audio waveforms into discrete token sequences and can reconstruct audio at high quality. It is commonly used for representation learning in music generation.
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The monograph connects neural audio codecs and autoencoders, autoregressive and masked token models, diffusion models and flow matching, sampling acceleration, and preference optimization, among other methods, with unified mathematical notation.
Neural audio codecs typically consist of an encoder, a quantizer, and a decoder: the encoder compresses the waveform into a low-frame-rate latent representation, the quantizer (such as residual vector quantization, RVQ) discretizes the continuous latents into multiple codebook indices, and the decoder reconstructs the waveform from the tokens.
Audio codecs pursue maintaining perceptual quality at extremely low bitrates, and are a prerequisite for autoregressive token models and masked token models to process music audio.
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The monograph connects neural audio codecs and autoencoders, autoregressive and masked token models, diffusion models and flow matching, sampling acceleration, and preference optimization, among other methods, with unified mathematical notation.
50%UnverifiedNeural audio codecs typically consist of an encoder, a quantizer, and a decoder: the encoder compresses the waveform into a low-frame-rate latent representation, the quantizer (such as residual vector quantization, RVQ) discretizes the continuous latents into multiple codebook indices, and the decoder reconstructs the waveform from the tokens.
50%UnverifiedAudio codecs pursue maintaining perceptual quality at extremely low bitrates, and are a prerequisite for autoregressive token models and masked token models to process music audio.
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