Speech Enhancement Using Deep Learning Methods: A Review

Jurnal Elektronika dan Telekomunikasi

Endang Suryawati¹  Ade Ramdan¹  Hilman Ferdinandus Pardede¹  Asri Rizki Yuliani¹  M. Faizal Amri² 

[1] Research Center for InformaticsIndonesian Institute of Sciences (LIPI);Technical Implementation Unit for Instrumental DevelopmentIndonesian Institute of Sciences (LIPI);

关键词: speech enhancement;    deep learning;    neural networks;    speech signal processing;    non-stationary noise;   

DOI  :  10.14203/jet.v21.19-26

来源: DOAJ

【 摘 要 】

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.

【 授权许可】

Unknown