A Novel Approach to Speech Enhancement Based on Deep Neural Networks
Minimum mean-square error (MMSE) approaches have been shown to achieve state-of-the-art performance on the task of speech enhancement. However, MMSE approaches lack the ability to accurately estimate non-stationary noise sources. In this paper, a long short-term memory fully convolutional network...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2022-05-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2022.02009 |
| Summary: | Minimum mean-square error (MMSE) approaches have been shown to achieve state-of-the-art performance on the
task of speech enhancement. However, MMSE approaches lack the ability to accurately estimate non-stationary
noise sources. In this paper, a long short-term memory fully convolutional network (LSTM-FCN) is utilized
to accurately estimate a priori signal-to-noise ratio (SNR) since the speech enhancement performance of
an MMSE approach improves with the accuracy of the used a priori SNR estimator. The proposed MMSE approach
makes no assumptions about the characteristics of the noise or the speech. MMSE approaches that utilize
the LSTM-FCN estimator are evaluated using the mean opinion score of the perceptual evaluation of speech
quality (PESQ) and the short-time objective intelligibility (STOI) measures of speech. The experimental
investigation shows that the speech enhancement performance of an MMSE approach that utilizes LSTM-FCN
estimator significantly increases. |
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| ISSN: | 1582-7445 1844-7600 |