Towards Evaluating Generative Audio: Insights from Neural Audio Codec Embedding Distances
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911391045124096 |
|---|---|
| author | Biswas, Arijit Villemoes, Lars |
| author_facet | Biswas, Arijit Villemoes, Lars |
| contents | Neural audio codecs (NACs) achieve low-bitrate compression by learning compact audio representations, which can also serve as features for perceptual quality evaluation. We introduce DACe, an enhanced, higher-fidelity version of the Descript Audio Codec (DAC), trained on diverse real and synthetic tonal data with balanced sampling. We systematically compare Fréchet Audio Distance (FAD) and Maximum Mean Discrepancy (MMD) on MUSHRA tests across speech, music, and mixed content. FAD consistently outperforms MMD, and embeddings from higher-fidelity NACs (such as DACe) show stronger correlations with human judgments. While CLAP LAION Music (CLAP-M) and OpenL3 Mel128 (OpenL3-128M) embeddings achieve higher correlations, NAC embeddings provide a practical zero-shot approach to audio quality assessment, requiring only unencoded audio for training. These results demonstrate the dual utility of NACs for compression and perceptually informed audio evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Evaluating Generative Audio: Insights from Neural Audio Codec Embedding Distances Biswas, Arijit Villemoes, Lars Audio and Speech Processing Neural audio codecs (NACs) achieve low-bitrate compression by learning compact audio representations, which can also serve as features for perceptual quality evaluation. We introduce DACe, an enhanced, higher-fidelity version of the Descript Audio Codec (DAC), trained on diverse real and synthetic tonal data with balanced sampling. We systematically compare Fréchet Audio Distance (FAD) and Maximum Mean Discrepancy (MMD) on MUSHRA tests across speech, music, and mixed content. FAD consistently outperforms MMD, and embeddings from higher-fidelity NACs (such as DACe) show stronger correlations with human judgments. While CLAP LAION Music (CLAP-M) and OpenL3 Mel128 (OpenL3-128M) embeddings achieve higher correlations, NAC embeddings provide a practical zero-shot approach to audio quality assessment, requiring only unencoded audio for training. These results demonstrate the dual utility of NACs for compression and perceptually informed audio evaluation. |
| title | Towards Evaluating Generative Audio: Insights from Neural Audio Codec Embedding Distances |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.18823 |