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arXiv · 2406.05128

Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis

Chin-Yun Yu·György Fazekas
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Abstract

Training the linear prediction (LP) operator end-to-end for audio synthesis in modern deep learning frameworks is slow due to its recursive formulation. In addition, frame-wise approximation as an acceleration method cannot generalise well to test time conditions where the LP is computed sample-wise. Efficient differentiable sample-wise LP for end-to-end training is the key to removing this barrier. We generalise the efficient time-invariant LP implementation from the GOLF vocoder to time-varying cases. Combining this with the classic source-filter model, we show that the improved GOLF learns LP coefficients and reconstructs the voice better than its frame-wise counterparts. Moreover, in our listening test, synthesised outputs from GOLF scored higher in quality ratings than the state-of-the-art differentiable WORLD vocoder.

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Chin-Yun Yu, György Fazekas. 2024-06-07. Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis. https://doi.org/10.21437/interspeech.2024-1187

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