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Blind bandwidth extension based on convolutional and recurrent deep neural networks

 
: Schmidt, K.; Edler, B.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech, and Signal Processing 2018. Proceedings : April 15-20, 2018, Calgary Telus Convention Center, Calgary, Alberty, Canada
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4658-8
ISBN: 978-1-5386-4657-1
ISBN: 978-1-5386-4659-5
S.5444-5448
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <2018, Calgary>
Englisch
Konferenzbeitrag
Fraunhofer IIS ()

Abstract
A blind bandwidth extension (BBWE) expands the bandwidth of telephone speech which often is limited to 0.2 to 3.4 kHz. The advantage is an increased perceived quality as well as an increased intelligibility. This work presents a BBWE similar to state-of-the-art bandwidth extensions like Intelligent Gap Filling with the difference that all processing is done in the decoder without the need of transmitting extra bits. Parameters like spectral envelope are estimated by a regressive Convolutional Deep Neuronal Network (CNN) with long short-term memory (LSTM). The system operates on frames of 20 ms without additional algorithmic delay and can be applied in state-of-the-art speech and audio codecs.

: http://publica.fraunhofer.de/dokumente/N-590257.html