Year: 2024
Type: conference-paper
Abstract: This work addresses the problem of latent space quantization in neural audio coding. A covariance analysis of latent space is performed on several pre-trained audio coding models (Lyra V2, EnCodec, AudioDec). It is proposed to truncate latent space dimension using a fixed linear transform. The Karhunen-Loève transform (KLT) is applied on learned residual vector quantization (RVQ) codebooks. The proposed method is applied in a backward-compatible way to EnCodec, and we show that quantization complexity and codebook storage are reduced (by 43.4%), with no noticeable difference in subjective AB tests.
Authors: Thomas Muller, Stéphane Ragot, Pierrick Philippe, Pascal Scalart
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4392606557