Background Noise Suppression in Audio File using LSTM Network

• Published in: International journal for research in applied science and engineering technology Vol. 10; no. 6; pp. 1310 - 1316
• Main Author: Patnaik, W. Shivani
• Format: Journal Article
• Language: English
• Published: 30.06.2022
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2022.44109

Abstract— In the realm of speech enhancement, noise suppression is a crucial problem. It is especially important in workfrom-home situations where noise reduction may improve communication quality and reduce the cognitive effort of video conferencing. As a result of the advent of deep neural networks, several novel ways for audio processing methods based on deep models have been presented. The goal of the project is to use a stacked Dual signal Transformation LSTM Network (DTLN) to combine both analysis and synthesis into one model. The proposed model consists of two separation cores, the first of which employs an Short Term Fourier Transformation (STFT) signal transformation and the second of which employs a learnt signal representation, This arrangement was designed to enable the second core to further improve the signal with phase information while the first core creates a strong magnitude estimation. Due to the complementarity of traditional and learnt features modifications, this combination might give good impacts while preserving a minimal computing footprint, in terms of computational complexity, the stacked network is far less than most previously suggested LSTM networks and assures real-time capabilities.