In speech enhancement, the use of supervised algorithms in the form of deep neural networks (DNNs) has become tremendously popular in recent years. The target function of the DNN (and the associated estimators) is often either a masking function applied to the noisy spectrum, or the clean log-spectrum. In this work, we show that both separate cost functions are unsuitable for dealing with narrowband noise, and propose a new composite estimator in the log-spectrum domain. The new technique relies on a single DNN that outputs both a masking function and an estimated log-spectrum. Both outputs are used for the composite enhancement. The proposed estimator demonstrates superior performance for speech utterances contaminated by additive narrowband noise, while maintaining the enhancement quality of the baseline algorithms for wideband noise.
|Title of host publication||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2020|
|Event||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain|
Duration: 4 May 2020 → 8 May 2020
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020|
|Period||4/05/20 → 8/05/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- Speech enhancement
- deep neural network
- narrowband noise
- single microphone