Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising
Denoising speech signals represent a challenging task due to the increasing number of applications and technologies currently implemented in communication and portable devices. In those applications, challenging environmental conditions such as background noise, reverberation, and other sound artif...
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Instituto Tecnológico de Costa Rica
2022-11-01
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| Series: | Tecnología en Marcha |
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| Online Access: | https://172.20.14.50/index.php/tec_marcha/article/view/6448 |
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| author | Marvin Coto-Jiménez Astryd González-Salazar Michelle Gutiérrez-Muñoz |
| author_facet | Marvin Coto-Jiménez Astryd González-Salazar Michelle Gutiérrez-Muñoz |
| author_sort | Marvin Coto-Jiménez |
| collection | DOAJ |
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Denoising speech signals represent a challenging task due to the increasing number of applications and technologies currently implemented in communication and portable devices. In those applications, challenging environmental conditions such as background noise, reverberation, and other sound artifacts can affect the quality of the signals. As a result, it also impacts the systems for speech recognition, speaker identification, and sound source localization, among many others. For denoising the speech signals degraded with the many kinds and possibly different levels of noise, several algorithms have been proposed during the past decades, with recent proposals based on deep learning presented as state-of-the-art, in particular those based on Long Short-Term Memory Networks (LSTM and Bidirectional-LSMT). In this work, a comparative study on different transfer learning strategies for reducing training time and increase the effectiveness of this kind of network is presented. The reduction in training time is one of the most critical challenges due to the high computational cost of training LSTM and BLSTM. Those strategies arose from the different options to initialize the networks, using clean or noisy information of several types. Results show the convenience of transferring information from a single case of denoising network to the rest, with a significant reduction in training time and denoising capabilities of the BLSTM networks.
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| format | Article |
| id | doaj-art-f99a5b276b2d41a2b1bf04ffe7d473f1 |
| institution | OA Journals |
| issn | 0379-3982 2215-3241 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Instituto Tecnológico de Costa Rica |
| record_format | Article |
| series | Tecnología en Marcha |
| spelling | doaj-art-f99a5b276b2d41a2b1bf04ffe7d473f12025-08-20T02:22:44ZengInstituto Tecnológico de Costa RicaTecnología en Marcha0379-39822215-32412022-11-0135810.18845/tm.v35i8.6448Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoisingMarvin Coto-JiménezAstryd González-SalazarMichelle Gutiérrez-Muñoz Denoising speech signals represent a challenging task due to the increasing number of applications and technologies currently implemented in communication and portable devices. In those applications, challenging environmental conditions such as background noise, reverberation, and other sound artifacts can affect the quality of the signals. As a result, it also impacts the systems for speech recognition, speaker identification, and sound source localization, among many others. For denoising the speech signals degraded with the many kinds and possibly different levels of noise, several algorithms have been proposed during the past decades, with recent proposals based on deep learning presented as state-of-the-art, in particular those based on Long Short-Term Memory Networks (LSTM and Bidirectional-LSMT). In this work, a comparative study on different transfer learning strategies for reducing training time and increase the effectiveness of this kind of network is presented. The reduction in training time is one of the most critical challenges due to the high computational cost of training LSTM and BLSTM. Those strategies arose from the different options to initialize the networks, using clean or noisy information of several types. Results show the convenience of transferring information from a single case of denoising network to the rest, with a significant reduction in training time and denoising capabilities of the BLSTM networks. https://172.20.14.50/index.php/tec_marcha/article/view/6448BLSTMdeep learningtransfer learningspeech processing |
| spellingShingle | Marvin Coto-Jiménez Astryd González-Salazar Michelle Gutiérrez-Muñoz Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising Tecnología en Marcha BLSTM deep learning transfer learning speech processing |
| title | Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising |
| title_full | Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising |
| title_fullStr | Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising |
| title_full_unstemmed | Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising |
| title_short | Assessing the effectiveness of transfer learning strategies in BLSTM networks for speech denoising |
| title_sort | assessing the effectiveness of transfer learning strategies in blstm networks for speech denoising |
| topic | BLSTM deep learning transfer learning speech processing |
| url | https://172.20.14.50/index.php/tec_marcha/article/view/6448 |
| work_keys_str_mv | AT marvincotojimenez assessingtheeffectivenessoftransferlearningstrategiesinblstmnetworksforspeechdenoising AT astrydgonzalezsalazar assessingtheeffectivenessoftransferlearningstrategiesinblstmnetworksforspeechdenoising AT michellegutierrezmunoz assessingtheeffectivenessoftransferlearningstrategiesinblstmnetworksforspeechdenoising |