Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding

Dianwen Ng, Kun Zhou, Yi-Wen Chao, Zhiwei Xiong, Bin Ma, Engsiong Chng
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:45934-45953, 2025.

Abstract

Achieving high-fidelity audio compression while preserving perceptual quality across diverse audio types remains a significant challenge in Neural Audio Coding (NAC). This paper introduces MUFFIN, a fully convolutional NAC framework that leverages psychoacoustically guided multi-band frequency reconstruction. Central to MUFFIN is the Multi-Band Spectral Residual Vector Quantization (MBS-RVQ) mechanism, which quantizes latent speech across different frequency bands. This approach optimizes bitrate allocation and enhances fidelity based on psychoacoustic studies, achieving efficient compression with unique perceptual features that separate content from speaker attributes through distinct codebooks. MUFFIN integrates a transformer-inspired convolutional architecture with proposed modified snake activation functions to capture fine frequency details with greater precision. Extensive evaluations on diverse datasets (LibriTTS, IEMOCAP, GTZAN, BBC) demonstrate MUFFIN’s ability to consistently surpass existing performance in audio reconstruction across various domains. Notably, a high-compression variant achieves an impressive SOTA 12.5 kHz rate while preserving reconstruction quality. Furthermore, MUFFIN excels in downstream generative tasks, demonstrating its potential as a robust token representation for integration with large language models. These results establish MUFFIN as a groundbreaking advancement in NAC and as the first neural psychoacoustic coding system. Speech demos and codes are available at https://demos46.github.io/muffin/ and https://github.com/dianwen-ng/MUFFIN.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-ng25b, title = {Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding}, author = {Ng, Dianwen and Zhou, Kun and Chao, Yi-Wen and Xiong, Zhiwei and Ma, Bin and Chng, Engsiong}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {45934--45953}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/ng25b/ng25b.pdf}, url = {https://proceedings.mlr.press/v267/ng25b.html}, abstract = {Achieving high-fidelity audio compression while preserving perceptual quality across diverse audio types remains a significant challenge in Neural Audio Coding (NAC). This paper introduces MUFFIN, a fully convolutional NAC framework that leverages psychoacoustically guided multi-band frequency reconstruction. Central to MUFFIN is the Multi-Band Spectral Residual Vector Quantization (MBS-RVQ) mechanism, which quantizes latent speech across different frequency bands. This approach optimizes bitrate allocation and enhances fidelity based on psychoacoustic studies, achieving efficient compression with unique perceptual features that separate content from speaker attributes through distinct codebooks. MUFFIN integrates a transformer-inspired convolutional architecture with proposed modified snake activation functions to capture fine frequency details with greater precision. Extensive evaluations on diverse datasets (LibriTTS, IEMOCAP, GTZAN, BBC) demonstrate MUFFIN’s ability to consistently surpass existing performance in audio reconstruction across various domains. Notably, a high-compression variant achieves an impressive SOTA 12.5 kHz rate while preserving reconstruction quality. Furthermore, MUFFIN excels in downstream generative tasks, demonstrating its potential as a robust token representation for integration with large language models. These results establish MUFFIN as a groundbreaking advancement in NAC and as the first neural psychoacoustic coding system. Speech demos and codes are available at https://demos46.github.io/muffin/ and https://github.com/dianwen-ng/MUFFIN.} }
Endnote
%0 Conference Paper %T Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding %A Dianwen Ng %A Kun Zhou %A Yi-Wen Chao %A Zhiwei Xiong %A Bin Ma %A Engsiong Chng %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-ng25b %I PMLR %P 45934--45953 %U https://proceedings.mlr.press/v267/ng25b.html %V 267 %X Achieving high-fidelity audio compression while preserving perceptual quality across diverse audio types remains a significant challenge in Neural Audio Coding (NAC). This paper introduces MUFFIN, a fully convolutional NAC framework that leverages psychoacoustically guided multi-band frequency reconstruction. Central to MUFFIN is the Multi-Band Spectral Residual Vector Quantization (MBS-RVQ) mechanism, which quantizes latent speech across different frequency bands. This approach optimizes bitrate allocation and enhances fidelity based on psychoacoustic studies, achieving efficient compression with unique perceptual features that separate content from speaker attributes through distinct codebooks. MUFFIN integrates a transformer-inspired convolutional architecture with proposed modified snake activation functions to capture fine frequency details with greater precision. Extensive evaluations on diverse datasets (LibriTTS, IEMOCAP, GTZAN, BBC) demonstrate MUFFIN’s ability to consistently surpass existing performance in audio reconstruction across various domains. Notably, a high-compression variant achieves an impressive SOTA 12.5 kHz rate while preserving reconstruction quality. Furthermore, MUFFIN excels in downstream generative tasks, demonstrating its potential as a robust token representation for integration with large language models. These results establish MUFFIN as a groundbreaking advancement in NAC and as the first neural psychoacoustic coding system. Speech demos and codes are available at https://demos46.github.io/muffin/ and https://github.com/dianwen-ng/MUFFIN.
APA
Ng, D., Zhou, K., Chao, Y., Xiong, Z., Ma, B. & Chng, E.. (2025). Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:45934-45953 Available from https://proceedings.mlr.press/v267/ng25b.html.

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