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Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport

22 Dec 2023arXiv:2312.14507archive 2025-07-28

Bernardo Torres, Geoffroy Peeters, Gaël Richard

In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a challenge when using standard audio-to-audio reconstruction loss, leading to reliance on external pitch trackers. To address this issue, we propose using a spectral loss function inspired by optimal transportation theory that minimizes the displacement of spectral energy. We validate this approach through an unsupervised autoencoding task that fits a harmonic template to harmonic signals. We jointly estimate the fundamental frequency and amplitudes of harmonics using a lightweight encoder and reconstruct the signals using a differentiable harmonic synthesizer. The proposed approach offers a promising direction for improving unsupervised parameter estimation in neural audio applications.

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Audio Signal Processingparameter estimation

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