KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912265425387520 |
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| author | Chung, Yoonjin Eu, Pilsun Lee, Junwon Choi, Keunwoo Nam, Juhan Chon, Ben Sangbae |
| author_facet | Chung, Yoonjin Eu, Pilsun Lee, Junwon Choi, Keunwoo Nam, Juhan Chon, Ben Sangbae |
| contents | Although being widely adopted for evaluating generated audio signals, the Fréchet Audio Distance (FAD) suffers from significant limitations, including reliance on Gaussian assumptions, sensitivity to sample size, and high computational complexity. As an alternative, we introduce the Kernel Audio Distance (KAD), a novel, distribution-free, unbiased, and computationally efficient metric based on Maximum Mean Discrepancy (MMD). Through analysis and empirical validation, we demonstrate KAD's advantages: (1) faster convergence with smaller sample sizes, enabling reliable evaluation with limited data; (2) lower computational cost, with scalable GPU acceleration; and (3) stronger alignment with human perceptual judgments. By leveraging advanced embeddings and characteristic kernels, KAD captures nuanced differences between real and generated audio. Open-sourced in the kadtk toolkit, KAD provides an efficient, reliable, and perceptually aligned benchmark for evaluating generative audio models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation Chung, Yoonjin Eu, Pilsun Lee, Junwon Choi, Keunwoo Nam, Juhan Chon, Ben Sangbae Sound Artificial Intelligence Machine Learning Audio and Speech Processing Although being widely adopted for evaluating generated audio signals, the Fréchet Audio Distance (FAD) suffers from significant limitations, including reliance on Gaussian assumptions, sensitivity to sample size, and high computational complexity. As an alternative, we introduce the Kernel Audio Distance (KAD), a novel, distribution-free, unbiased, and computationally efficient metric based on Maximum Mean Discrepancy (MMD). Through analysis and empirical validation, we demonstrate KAD's advantages: (1) faster convergence with smaller sample sizes, enabling reliable evaluation with limited data; (2) lower computational cost, with scalable GPU acceleration; and (3) stronger alignment with human perceptual judgments. By leveraging advanced embeddings and characteristic kernels, KAD captures nuanced differences between real and generated audio. Open-sourced in the kadtk toolkit, KAD provides an efficient, reliable, and perceptually aligned benchmark for evaluating generative audio models. |
| title | KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.15602 |