Affiliations: Department of Electronics and Communicaton Engineering, Siksha ‘O’ Anusandhan University (Deemed to be University), Bhubaneswar, India
Correspondence:
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Corresponding author. Mihir Narayan Mohanty, Department of Electronics and Communicaton Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar, India. E-mail: [email protected].
Abstract: In this digitized world, the demand of users emphasizes the quality and accuracy. Practically, all variants of signals are analog in nature along with contaminated with noise. In this paper, speech signal is considered. Basically speech signal varies from person to person and time to time. It requires enhancement of the signal for different applications like engineering, medicine and social purposes. Reduction of noise as well as redundant data from the signal can be produced with enhanced versions. As the speech is of nonstationary in nature, in the initial phase, it is processed and normalized. To analyze the speech signal, spectral domain is most suitable and has been utilized. For this purpose, Discrete Cosine Transform (DCT-II) is used. As it has the advantage over other transforms and the calculation is simpler, DCT-II coefficients are further used for Deep Neural Network (DNN) model to reduce the noise and enhance the signal. So that the signal of any environment and of any amount can be enhanced using this model. 100 sentences have been collected form both males and females of 5 each. The sentences have been uttered by the corresponding males and females, 10 sentences each. Though DCT-II and DNN have been applied by many researchers for signal features and image classification, the same have been utilized here for speech enhancement, which is the novelty of this work. The results found better than the other methods applied earlier and it can be best utilized for any real time application. In the result section, the visual inspection is exhibited along with the comparison values. The measuring parameters show its efficacy.
Keywords: Discrete cosine transform, deep neural network, speech enhancement, perceptual evaluation of speech quality, segmental signal-to-noise
