Authors

* External authors

Venue

Date

Share

VRVQ: Variable Bitrate Residual Vector Quantization for Audio Compression

Yunkee Chae

Woosung Choi

Yuhta Takida

Junghyun Koo*

Yukara Ikemiya

Zhi Zhong*

Kin Wai Cheuk

Marco A. Martínez-Ramírez

Kyogu Lee*

Wei-Hsiang Liao

Yuki Mitsufuji

* External authors

ICASSP-25

2025

Abstract

Recent state-of-the-art neural audio compression models have progressively adopted residual vector quantization (RVQ). Despite this success, these models employ a fixed number of codebooks per frame, which can be suboptimal in terms of rate-distortion tradeoff, particularly in scenarios with simple input audio, such as silence. To address this limitation, we propose variable bitrate RVQ (VRVQ) for audio codecs, which allows for more efficient coding by adapting the number of codebooks used per frame. Furthermore, we propose a gradient estimation method for the non-differentiable masking operation that transforms from the importance map to the binary importance mask, improving model training via a straight-through estimator. We demonstrate that the proposed training framework achieves superior results compared to the baseline method and shows further improvement when applied to the current state-of-the-art codec.

Related Publications

Music Arena: Live Evaluation for Text-to-Music

NeurIPS, 2025
Yonghyun Kim, Wayne Chi, Anastasios N. Angelopoulos, Wei-Lin Chiang, Koichi Saito, Shinji Watanabe, Yuki Mitsufuji, Chris Donahue

We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare…

Large-Scale Training Data Attribution for Music Generative Models via Unlearning

NeurIPS, 2025
Woosung Choi, Junghyun Koo*, Kin Wai Cheuk, Joan Serrà, Marco A. Martínez-Ramírez, Yukara Ikemiya, Naoki Murata, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji

This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific mod…

Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion

NeurIPS, 2025
Michail Dontas, Yutong He, Naoki Murata, Yuki Mitsufuji, J. Zico Kolter*, Ruslan Salakhutdinov*

Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thu…

  • HOME
  • Publications
  • VRVQ: Variable Bitrate Residual Vector Quantization for Audio Compression

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.