Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909816655446016 |
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| author | Alakuijala, Jyrki Bruse, Martin Boukortt, Sami Coldenhoff, Jozef Marus Cernak, Milos |
| author_facet | Alakuijala, Jyrki Bruse, Martin Boukortt, Sami Coldenhoff, Jozef Marus Cernak, Milos |
| contents | This paper introduces Zimtohrli, a novel, full-reference audio similarity metric designed for efficient and perceptually accurate quality assessment. In an era dominated by computationally intensive deep learning models and proprietary legacy standards, there is a pressing need for an interpretable, psychoacoustically-grounded metric that balances performance with practicality. Zimtohrli addresses this gap by combining a 128-bin gammatone filterbank front-end, which models the frequency resolution of the cochlea, with a unique non-linear resonator model that mimics the human eardrum's response to acoustic stimuli. Similarity is computed by comparing perceptually-mapped spectrograms using modified Dynamic Time Warping (DTW) and Neurogram Similarity Index Measure (NSIM) algorithms, which incorporate novel non-linearities to better align with human judgment. Zimtohrli achieves superior performance to the baseline open-source ViSQOL metric, and significantly narrows the performance gap with the latest commercial POLQA metric. It offers a compelling balance of perceptual relevance and computational efficiency, positioning it as a strong alternative for modern audio engineering applications, from codec development to the evaluation of generative audio systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_26133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric Alakuijala, Jyrki Bruse, Martin Boukortt, Sami Coldenhoff, Jozef Marus Cernak, Milos Audio and Speech Processing This paper introduces Zimtohrli, a novel, full-reference audio similarity metric designed for efficient and perceptually accurate quality assessment. In an era dominated by computationally intensive deep learning models and proprietary legacy standards, there is a pressing need for an interpretable, psychoacoustically-grounded metric that balances performance with practicality. Zimtohrli addresses this gap by combining a 128-bin gammatone filterbank front-end, which models the frequency resolution of the cochlea, with a unique non-linear resonator model that mimics the human eardrum's response to acoustic stimuli. Similarity is computed by comparing perceptually-mapped spectrograms using modified Dynamic Time Warping (DTW) and Neurogram Similarity Index Measure (NSIM) algorithms, which incorporate novel non-linearities to better align with human judgment. Zimtohrli achieves superior performance to the baseline open-source ViSQOL metric, and significantly narrows the performance gap with the latest commercial POLQA metric. It offers a compelling balance of perceptual relevance and computational efficiency, positioning it as a strong alternative for modern audio engineering applications, from codec development to the evaluation of generative audio systems. |
| title | Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.26133 |