Neural Audio Codecs

Neural audio codecs compress sound by mapping waveforms into compact latent representations, often using residual vector quantization, then reconstructing audio with a learned decoder. Current approaches balance perceptual fidelity with bitrate, intelligibility, speaker identity, and attributes such as emotion or pitch, using perceptual losses, self-supervised features, and separate semantic and acoustic streams. Researchers also design codec tokens for downstream generation with language models, while developing streaming codecs, variable frame rates, and hybrid discrete-continuous representations. A central challenge is that lower rates and more predictable tokens can lose acoustic detail, and quality metrics do not always reveal whether information needed for speech or other tasks survives compression.