SUNAC: Source-aware Unified Neural Audio Codec
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908666681098240 |
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| author | Aihara, Ryo Masuyama, Yoshiki Paissan, Francesco Germain, François G. Wichern, Gordon Roux, Jonathan Le |
| author_facet | Aihara, Ryo Masuyama, Yoshiki Paissan, Francesco Germain, François G. Wichern, Gordon Roux, Jonathan Le |
| contents | Neural audio codecs (NACs) provide compact representations that can be leveraged in many downstream applications, in particular large language models. Yet most NACs encode mixtures of multiple sources in an entangled manner, which may impede efficient downstream processing in applications that need access to only a subset of the sources (e.g., analysis of a particular type of sound, transcription of a given speaker, etc). To address this, we propose a source-aware codec that encodes individual sources directly from mixtures, conditioned on source type prompts. This enables user-driven selection of which source(s) to encode, including separately encoding multiple sources of the same type (e.g., multiple speech signals). Experiments show that our model achieves competitive resynthesis and separation quality relative to a cascade of source separation followed by a conventional NAC, with lower computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SUNAC: Source-aware Unified Neural Audio Codec Aihara, Ryo Masuyama, Yoshiki Paissan, Francesco Germain, François G. Wichern, Gordon Roux, Jonathan Le Audio and Speech Processing Signal Processing Neural audio codecs (NACs) provide compact representations that can be leveraged in many downstream applications, in particular large language models. Yet most NACs encode mixtures of multiple sources in an entangled manner, which may impede efficient downstream processing in applications that need access to only a subset of the sources (e.g., analysis of a particular type of sound, transcription of a given speaker, etc). To address this, we propose a source-aware codec that encodes individual sources directly from mixtures, conditioned on source type prompts. This enables user-driven selection of which source(s) to encode, including separately encoding multiple sources of the same type (e.g., multiple speech signals). Experiments show that our model achieves competitive resynthesis and separation quality relative to a cascade of source separation followed by a conventional NAC, with lower computational cost. |
| title | SUNAC: Source-aware Unified Neural Audio Codec |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2511.16126 |