# Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric

arXiv: https://arxiv.org/abs/2509.26133 · HTML: https://mlsanity.com/paper/2509.26133/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2025.

## Built on by (most cited first, 6 of 6)

- [Summary of The Inaugural Music Source Restoration Challenge](https://mlsanity.com/paper/2601.04343/) (2026)
- [Music Source Restoration with Ensemble Separation and Targeted Reconstruction](https://mlsanity.com/paper/2603.16926/) (2026)
- [The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions](https://mlsanity.com/paper/2606.14466/) (2026)
- [DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration](https://mlsanity.com/paper/2606.24127/) (2026)
- [Differentiable Digital Signal Processing Mixture Model-Guided Diffusion for Synthesis Parameter Estimation from Harmonic Sound Mixtures](https://mlsanity.com/paper/2609.14427/) (2026)
- [Multichannel Audio Quality Assessment: Extending Pretrained Perceptual Models to Spatial Audio](https://mlsanity.com/paper/2609.37116/) (2026)

## Abstract (arXiv)

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.