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In neural audio signal processing, pitch conditioning has been used toenhance the performance of synthesizers. However, jointly training pitchestimators and synthesizers is a challenge when using standard audio-to-audioreconstruction loss, leading to reliance on external pitch trackers. To addressthis issue, we propose using a spectral loss function inspired by optimaltransportation theory that minimizes the displacement of spectral energy. Wevalidate this approach through an unsupervised autoencoding task that fits aharmonic template to harmonic signals. We jointly estimate the fundamentalfrequency and amplitudes of harmonics using a lightweight encoder andreconstruct the signals using a differentiable harmonic synthesizer. Theproposed approach offers a promising direction for improving unsupervisedparameter estimation in neural audio applications.

Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport