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A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet

arXiv 1903.12087 ↗First posted 28 Mar 2019eess.AS cs.LG cs.SDcited by 16

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Abstract (arXiv)

Neural speech synthesis algorithms are a promising new approach for coding speech at very low bitrate. They have so far demonstrated quality that far exceeds traditional vocoders, at the cost of very high complexity. In this work, we present a low-bitrate neural vocoder based on the LPCNet model. The use of linear prediction and sparse recurrent networks makes it possible to achieve real-time operation on general-purpose hardware. We demonstrate that LPCNet operating at 1.6 kb/s achieves significantly higher quality than MELP and that uncompressed LPCNet can exceed the quality of a waveform codec operating at low bitrate. This opens the way for new codec designs based on neural synthesis models.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Mar 2019.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

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