Low-Complexity Real-Time Neural Network for Blind Bandwidth Extension of Wideband Speech

Esteban Gómez, Mohammad Hassan Vali, Tom Bäckström

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 realtime 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 realtime 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.