Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis
<|reference_start|>Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis: 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.<|reference_end|>
@article{yu2024differentiable,
    title={Differentiable Time-Varying Linear Prediction in the Context of
  End-to-End Analysis-by-Synthesis},
    author={Chin-Yun Yu, Gy"orgy Fazekas},
    journal={arXiv preprint arXiv:2406.05128},
    year={2024},
    doi={10.21437/Interspeech.2024-1187},
    archivePrefix={arXiv},
    eprint={2406.05128},
    primaryClass={eess.AS cs.SD}
}