An Improved Method for Speech Enhancement Using Convolutional Neural Network Approach

In the speech processing domain Speech enhancement is one of the most widely used techniques. With the development of deep neural networks and the availability of powerful hardware, multiple deep learning-based speech enhancement models have come up in recent years. In this work, the speech enhancem...

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Podrobná bibliografia
Vydané v:	2022 International Conference on Signal and Information Processing (IConSIP) s. 1 - 6
Hlavní autori:	T N, Mahesh Kumar, Hegde, Pradyoth, K T, Deepak, Narasimhadhan, A V
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 26.08.2022
Predmet:	Convolutional neural network Deep learning Denoising auto-encoder Feedforward neural networks Hardware Noise reduction Speech enhancement Topology Transforms
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Popis
Shrnutí:	In the speech processing domain Speech enhancement is one of the most widely used techniques. With the development of deep neural networks and the availability of powerful hardware, multiple deep learning-based speech enhancement models have come up in recent years. In this work, the speech enhancement technique using a Convolutional Neural Network(CNN) as Denoising Autoencoders (DAEs) is investigated and compared with the conventional feed-forward topology. Further, The proposed model is analyzed at various SNR levels to process the corrupted english speech and also tested on unseen speech data which includes additional SNR levels. It is observed from simulation results that the proposed model outperforms the existing model in terms of Perceptual Evaluation of Speech Quality (PESQ) and Log Spectral Distance (LSD). The network achieved 3% higher scores than feed-forward neural networks, and it is found that the convolutional DAEs perform better than feed-forward counterparts.
DOI:	10.1109/ICoNSIP49665.2022.10007477