Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport
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Abstract (arXiv)
In this paper, we propose an Optimal Transport objective for learning one-dimensional translation-equivariant systems and demonstrate its applicability to single pitch estimation. Our method provides a theoretically grounded, more numerically stable, and simpler alternative for training state-of-the-art self-supervised pitch estimators.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 2 Aug 2025.
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References · 12
- 2018Computational Optimal Transportcited by 780
- 2018CREPE: A Convolutional Representation for Pitch Estimationcited by 110
- 2019SPICE: Self-supervised Pitch Estimationcited by 27
- 2019The Wasserstein-Fourier Distance for Stationary Time Seriescited by 18
- 2023PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objectivecited by 18
- 2023Cross-domain Neural Pitch and Periodicity Estimationcited by 16
- 2016Optimal spectral transportation with application to music transcriptioncited by 12
- 2022Equivariant Self-Supervision for Musical Tempo Estimationcited by 9
- 2023Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transportcited by 8
- 2024STONE: Self-supervised Tonality Estimatorcited by 8
- 2024Toward Fully Self-Supervised Multi-Pitch Estimationcited by 5
- 2025S-KEY: Self-supervised Learning of Major and Minor Keys from Audiocited by 4
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