@inproceedings{zhu-etal-2026-csavocoder,
title = "{CSAV}ocoder: A Causal Spatial Audio Vocoder Towards Real-Time Spatial Audio Generation",
author = "Zhu, Zhiyuan and
Wang, Han and
Guo, Wenxiang and
Zhang, Yu and
Pan, Changhao and
Yang, Rui and
Zhao, Zhou",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.496/",
pages = "11394--11416",
ISBN = "979-8-89176-459-0",
abstract = "Spatial audio vocoders are able to convertmel-spectrograms produced by generative mod-els into spatial audio waveforms. Most neural vocoders are designed for monaural audio, and direct extensions to spatial audio can de-grade spatial quality by ignoring inter-channelcues. We present CSAVocoder, a causal GAN-based spatial audio vocoder that jointly optimizes waveform fidelity and spatial rendering. Our framework introduces a Spatial Adaptor that fuses multi-channel mel-spectrograms with dynamic source-listener pose information, together with a spatial consistency discriminator that supervises inter-channel cues.To meet real-time requirements, we design a strictly causal, stateful generator that supports efficient streaming inference with constant memory overhead. Experiments on largescale spatial audio datasets show that CSAVocoder improves spatial fidelity at competitive audio quality and real-time performance.Our project page is at: https://github.com/MM-Speech/CSAVocoder."
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<abstract>Spatial audio vocoders are able to convertmel-spectrograms produced by generative mod-els into spatial audio waveforms. Most neural vocoders are designed for monaural audio, and direct extensions to spatial audio can de-grade spatial quality by ignoring inter-channelcues. We present CSAVocoder, a causal GAN-based spatial audio vocoder that jointly optimizes waveform fidelity and spatial rendering. Our framework introduces a Spatial Adaptor that fuses multi-channel mel-spectrograms with dynamic source-listener pose information, together with a spatial consistency discriminator that supervises inter-channel cues.To meet real-time requirements, we design a strictly causal, stateful generator that supports efficient streaming inference with constant memory overhead. Experiments on largescale spatial audio datasets show that CSAVocoder improves spatial fidelity at competitive audio quality and real-time performance.Our project page is at: https://github.com/MM-Speech/CSAVocoder.</abstract>
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%0 Conference Proceedings
%T CSAVocoder: A Causal Spatial Audio Vocoder Towards Real-Time Spatial Audio Generation
%A Zhu, Zhiyuan
%A Wang, Han
%A Guo, Wenxiang
%A Zhang, Yu
%A Pan, Changhao
%A Yang, Rui
%A Zhao, Zhou
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhu-etal-2026-csavocoder
%X Spatial audio vocoders are able to convertmel-spectrograms produced by generative mod-els into spatial audio waveforms. Most neural vocoders are designed for monaural audio, and direct extensions to spatial audio can de-grade spatial quality by ignoring inter-channelcues. We present CSAVocoder, a causal GAN-based spatial audio vocoder that jointly optimizes waveform fidelity and spatial rendering. Our framework introduces a Spatial Adaptor that fuses multi-channel mel-spectrograms with dynamic source-listener pose information, together with a spatial consistency discriminator that supervises inter-channel cues.To meet real-time requirements, we design a strictly causal, stateful generator that supports efficient streaming inference with constant memory overhead. Experiments on largescale spatial audio datasets show that CSAVocoder improves spatial fidelity at competitive audio quality and real-time performance.Our project page is at: https://github.com/MM-Speech/CSAVocoder.
%U https://aclanthology.org/2026.emnlp-main.496/
%P 11394-11416
Markdown (Informal)
[CSAVocoder: A Causal Spatial Audio Vocoder Towards Real-Time Spatial Audio Generation](https://aclanthology.org/2026.emnlp-main.496/) (Zhu et al., EMNLP 2026)
ACL