An intelligent speech enhancement model using enhanced heuristic-based residual convolutional neural network with encoder-decoder architecture
As the listening capacity exists in humans, they are facing the critical issues of understanding the speeches even in the presence of some background or other noises in the world. To diminish the noises, Speech Enhancement (SE) is the process of improving the quality of the speech signal by applying...
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| Veröffentlicht in: | International journal of speech technology Jg. 27; H. 3; S. 637 - 656 |
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| Abstract | As the listening capacity exists in humans, they are facing the critical issues of understanding the speeches even in the presence of some background or other noises in the world. To diminish the noises, Speech Enhancement (SE) is the process of improving the quality of the speech signal by applying some techniques without degrading any such information. Hence, it is used for hearing-aid people, speech recognition, etc. Recent researchers have developed some works to increase speech intelligibility. Substantial success has been achieved by executing the supervised learning methods. Nevertheless, the existing process incurs such shortcomings as attaining maximum error, computation burden, and so on. To overcome that, certain deep learning methodologies are immensely involved in SE for determining the spectrogram magnitude when reconstructing the signal source by removing the noise. Thus, it also becomes a more challenging task to acquire a clean speech signal. Though these methods aim to present the speech as more clear and intelligible, it may arise such intricacies that degrade the quality and efficiency. As it contains beneficial structure and resources, still it is in the scope of developing the novel SE model. To conquer these complexities, a successful SE task is offered utilizing deep learning in this paper. This recommended work performs the SE which employs deep learning to denoise a noisy speech to generate a quality speech. At first, the speech signal that contains noises such as cooler noise or fan noise, restaurant noise, railway station noise, factory noise, traffic in the journey, bus-stand noise, cinema theater noise, and clouding areas noise are gathered from the standard online sources. After that, this noisy speech signal is forwarded to Adaptive Residual Convolutional Neural Networks with Encoder-Decoder (ARCNNetED) architecture, where the parameters involved in this framework are optimized with the support of Random Revised Drawer Algorithm (RRDA). Thus, the noise presented in the input speech signal is completely removed by the suggested ARCNNetED and the quality of the speech also is enhanced. Finally, the performance of the suggested speech enhancement approach is evaluated over various traditional tasks with the support of several metrics. The findings of the developed model show better performance in terms of MAE, RMSE, and PSNR showing the value of 0.03, 0.19, and 62.26. This analysis significantly handles the error rate to offer accurate outcomes in the speech recognition framework. |
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| AbstractList | As the listening capacity exists in humans, they are facing the critical issues of understanding the speeches even in the presence of some background or other noises in the world. To diminish the noises, Speech Enhancement (SE) is the process of improving the quality of the speech signal by applying some techniques without degrading any such information. Hence, it is used for hearing-aid people, speech recognition, etc. Recent researchers have developed some works to increase speech intelligibility. Substantial success has been achieved by executing the supervised learning methods. Nevertheless, the existing process incurs such shortcomings as attaining maximum error, computation burden, and so on. To overcome that, certain deep learning methodologies are immensely involved in SE for determining the spectrogram magnitude when reconstructing the signal source by removing the noise. Thus, it also becomes a more challenging task to acquire a clean speech signal. Though these methods aim to present the speech as more clear and intelligible, it may arise such intricacies that degrade the quality and efficiency. As it contains beneficial structure and resources, still it is in the scope of developing the novel SE model. To conquer these complexities, a successful SE task is offered utilizing deep learning in this paper. This recommended work performs the SE which employs deep learning to denoise a noisy speech to generate a quality speech. At first, the speech signal that contains noises such as cooler noise or fan noise, restaurant noise, railway station noise, factory noise, traffic in the journey, bus-stand noise, cinema theater noise, and clouding areas noise are gathered from the standard online sources. After that, this noisy speech signal is forwarded to Adaptive Residual Convolutional Neural Networks with Encoder-Decoder (ARCNNetED) architecture, where the parameters involved in this framework are optimized with the support of Random Revised Drawer Algorithm (RRDA). Thus, the noise presented in the input speech signal is completely removed by the suggested ARCNNetED and the quality of the speech also is enhanced. Finally, the performance of the suggested speech enhancement approach is evaluated over various traditional tasks with the support of several metrics. The findings of the developed model show better performance in terms of MAE, RMSE, and PSNR showing the value of 0.03, 0.19, and 62.26. This analysis significantly handles the error rate to offer accurate outcomes in the speech recognition framework. |
| Author | Balasubrahmanyam, M. Valarmathi, R. S. |
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| Cites_doi | 10.1109/ACCESS.2020.2982212 10.1109/TASLP.2019.2933698 10.1007/s10489-023-04571-y 10.1109/TASLP.2020.2991537 10.1109/TASLP.2018.2870725 10.1109/TASLP.2021.3092838 10.1109/TASLP.2019.2910638 10.1109/TASLP.2020.2998279 10.1109/TASLP.2020.3036611 10.1109/ICSSIT46314.2019.8987950 10.1109/TASLP.2018.2876171 10.1109/TASLP.2015.2498101 10.1007/s00500-021-06291-2 10.1109/TASLP.2021.3082282 10.1109/LWC.2021.3095383 10.1109/TASLP.2020.3025638 10.1016/j.inffus.2019.08.008 10.1109/ACCESS.2021.3056711 10.1016/j.cie.2021.107250 10.1016/j.knosys.2022.109215 10.1109/TASLP.2022.3231700 10.1016/j.tcs.2022.08.017 10.3390/biomimetics8020239 10.1109/TASLP.2019.2940662 10.1109/ACCESS.2023.3253719 10.1109/LSP.2021.3128374 10.1109/LSP.2019.2951950 10.1109/LSP.2022.3200581 |
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| Keywords | Speech enhancement Divergent noise signals Speech signal Adaptive residual convolutional neural networks with encoder-decoder Random revised drawer algorithm |
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| SubjectTerms | Acknowledgment Algorithms Artificial Intelligence Artificial neural networks Background noise Computation Deep learning Encoders-Decoders Engineering Error analysis Hearing aids Heuristic Intelligence Intelligibility Learning Machine learning Neural networks Noise Performance evaluation Railway stations Restaurants Root-mean-square errors Signal quality Signal,Image and Speech Processing Social Sciences Speech Speech enhancement Speech processing Speech recognition Speeches Supervised learning Voice recognition Work |
| Title | An intelligent speech enhancement model using enhanced heuristic-based residual convolutional neural network with encoder-decoder architecture |
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