The Use of Deep Learning in Speech Enhancement

• Autorzy: Ram R. , Mohanty M. N.

Identyfikatory

DOI

10.15439/2017KM40

• Konferencja: International Conference on Information Technology and Knowledge Management (1 ; 22-23.12.2017 ; New Delhi, India)
• Języki publikacji: EN

Abstrakty

EN

Deep learning is an emerging area in current scenario. Mostly, Convolutional Neural Network (CNN) and Deep Belief Network (DBN) are used as the model in deep learning. It is termed as Deep Neural Network (DNN). The use of DNN is widely spread in many applications, exclusively for detection and classification purpose. In this paper, authors have used the same network for signal enhancement purpose. Speech is considered for the input signal with noise. The model of DNN is used with two layers. It has been compared with the ADALINE model to prove its efficacy.

Słowa kluczowe

EN

speech enhancement; neural networks; adaptive linear neuron; deep neural network; signal-to-noise ratio; perceptual evaluation of speech quality

PL

doskonalenie mowy; sieci neuronowe; adaptacyjny neuron liniowy; głęboka sieć neuronowa; stosunek sygnału do szumu; percepcyjna ocena jakości mowy

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2018
• Tom: Vol. 14
• Strony: 107--111
• Opis fizyczny: Bibliogr. 17 poz., wykr., il.

Twórcy

autor

Ram R.

• Electronics & Communication Engineering, ITER, Siksha 'O' Anusandhan, Deemed to be University, Bhubaneswar, India

autor

Mohanty M. N.

• Electronics & Communication Engineering, ITER, Siksha 'O' Anusandhan, Deemed to be University, Bhubaneswar, India

Bibliografia

• 1. P. Loizou, Speech Enhancement: Theory and Practice. CRC Press, 2007.
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• 3. R.Ram, M.N.Mohanty, Performance Analysis of Adaptive Algorithms for Speech Enhancement Applications, Indian Journal of Science and Technology 9(44), 2016.
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• 5. L.B.Fah, A.Hussain & S.A.Samad, Speech Enhancement by Noise Cancellation Using Neural Network, IEEE Conf., 2000.
• 6. R.Ram, M.N.Mohanty, Fractional DCT ADALINE Method for Speech Enhancement, Int. Conf. on Machine Learning & Computational Intelligence, 2017. (Accepted)
• 7. A.Prieto, B.Prieto, E.M.Ortigosa, E.Ros, F. Pelayo, J.Ortega, I. Rojas, Neural Networks: An Overview of Early Research, Current Frameworks and New Challenges, Neurocomputing 214, pp.242–268, 2016.
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• 10. Y.Xu, J.Du, L.R.Dai, and C.H.Lee, An Experimental Study on Speech Enhancement Based on Deep Neural Networks, IEEE Signal Processing Letters 21(1), 2014.
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• 12. R.Li, Y.Liu, Y.Shi, L.Dong, W.Cui, ILMSAF based Speech Enhancement with DNN and Noise Classification, Speech Communication 85, pp.53–70, 2016.
• 13. Y.Li, S.Kang, Deep Neural Network-Based Linear Predictive Parameter Estimations for Speech Enhancement, IET Signal Process.11 (4), pp.469-476, 2017.
• 14. T.Goehring, F.Bolner, J.Monaghan, B.Dijk, A.Zarowski, S.Bleeck, Speech Enhancement Based on Neural Networks Improves Speech Intelligibility in Noise for Cochlear Implant Users, Hearing Research 344, pp.183-194, 2017.
• 15. Y.Koizumi, K.Niwa, Y.Hioka, K.Kobayashi and Y.Haneda, DNN-Based Source Enhancement Self-Optimized By Reinforcement Learning Using Sound Quality Measurements, IEEE Conf., 2017.
• 16. R.Ram, M.N.Mohanty, Deep Neural Network based Speech Enhancement. Int. Conf. On Cognitive Informatics & Soft Computing, 2017. (Accepted)
• 17. J.C.Hou, S.S.Wang, Y.H.Lai, J.C.Lin, Y Tsao, H.W.Chang, H.M.Wang, Audio-Visual Speech Enhancement using Deep Neural Networks, Signal and Information Processing Association Annual Summit and Conference (APSIPA), Asia-Pacific, IEEE, 2016.

Uwagi

1. Preface

2. Technical Session: First International Conference on Information Technology and Knowledge Management