Low-complexity Real-time Neural Network for Blind Bandwidth Extension of Wideband Speech

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Abstract

Speech is streamed at 16 kHz or lower sample rates in many applications (e.g. VoIP, Bluetooth headsets). Extending its bandwidth can produce significant quality improvements. We introduce BBWEXNet, a lightweight neural network that performs blind bandwidth extension of speech from 16 kHz (wideband) to 48 kHz (fullband) in real-time in CPU. Our low latency approach allows running the model with a maximum algorithmic delay of 16 ms, enabling end-to-end communication in streaming services and scenarios where the GPU is busy or unavailable. We propose a series of optimizations that take advantage of the U-Net architecture and vector quantization methods commonly used in speech coding, to produce a model whose performance is comparable to previous real-time solutions, but approximately halving the memory footprint and computational cost. Moreover, we show that the model complexity can be further reduced with a marginal impact on the perceived output quality.
Original languageEnglish
Title of host publication31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PublisherEuropean Association For Signal and Imag Processing
Pages31-35
Number of pages5
ISBN (Electronic)978-94-645936-0-0
DOIs
Publication statusPublished - 4 Sept 2023
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Helsinki, Finland
Duration: 4 Sept 20238 Sept 2023
Conference number: 31
https://eusipco2023.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Country/TerritoryFinland
CityHelsinki
Period04/09/202308/09/2023
Internet address

Keywords

  • bandwidth extension
  • speech processing
  • real-time
  • deep learning

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