Enhancing into the Codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders

Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech out-put. However, these models are tightly coupled with speech content, and produce unintended outputs in noisy conditions. Ba...

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Vydané v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 711 - 715
Hlavní autori: Casebeer, Jonah, Vale, Vinjai, Isik, Umut, Valin, Jean-Marc, Giri, Ritwik, Krishnaswamy, Arvindh
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.06.2021
Predmet:
audio compression
Codecs
Convolution
Convolutional codes
Decoding
Speech coding
Speech enhancement
Training
ISSN:2379-190X
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Popis
Shrnutí:Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech out-put. However, these models are tightly coupled with speech content, and produce unintended outputs in noisy conditions. Based on VQ-VAE autoencoders with WaveRNN decoders, we develop compressor-enhancer encoders and accompanying decoders, and show that they operate well in noisy conditions. We also observe that a compressor-enhancer model performs better on clean speech inputs than a compressor model trained only on clean speech.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414605