Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Speech Enhancement

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    Wavelet based Noise Reduction Techniques for Real Time Speech Enhancement
    (Institute of Electrical and Electronics Engineers Inc., 2018) Ravi, B.R.; Deepu, S.P.; Ramesh Kini, M.; Sumam David, S.
    Fixed noise suppression techniques are generally used for speech enhancement in different low power real time systems. In this paper, we propose a modified adaptive system for classification of speech signals and noise reduction based on multi-band techniques. It involves initial identification of incoming speech segments as clean speech, speech in noise or pure noise. For the noisy speech segments, background noise classification is carried out using different wavelet-based feature sets. Noise Reduction system consists of removal of adaptive stationary noise and non-stationary noise based on classified noise type. Simulation results show that the proposed system provides optimal noise reduction and better speech quality with reduced computational complexity in adverse noisy environments. © 2018 IEEE.
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    An Improved Method for Speech Enhancement Using Convolutional Neural Network Approach
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mahesh Kumar, T.N.; Hegde, P.; Deepak, K.T.; Narasimhadhan, A.V.
    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. © 2022 IEEE.