Infant Cry Detection With Lightweight Wavelet Scattering Networks
Devices equipped with algorithms for detecting infant cries allow parents to respond immediately to their crying babies. However, improving detection performance often requires complex algorithms that consume more computing resources, leading to increased power consumption and prices. In this study,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10335657/ |
| _version_ | 1827585376434257920 |
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| author | Haitao Cao Haishan Chen Junying Yuan |
| author_facet | Haitao Cao Haishan Chen Junying Yuan |
| author_sort | Haitao Cao |
| collection | DOAJ |
| description | Devices equipped with algorithms for detecting infant cries allow parents to respond immediately to their crying babies. However, improving detection performance often requires complex algorithms that consume more computing resources, leading to increased power consumption and prices. In this study, we propose a novel approach that leverages first-order Wavelet Scattering Coefficients (WSC) as translation-invariant and deformation-stable representations of infant crying sounds. Based on this approach, we introduce an end-to-end Deep Neural Network (DNN) architecture designed to detect crying using merely 17K parameters and 22.7M MACs. The accuracy results, with a 96.98% accuracy rate on open-source datasets, demonstrate the effectiveness and robustness of our model for detecting infant cries in real-world environments. |
| first_indexed | 2024-03-08T23:44:39Z |
| format | Article |
| id | doaj.art-5e3b157932cd44eca0626eb72b7129e6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T23:44:39Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5e3b157932cd44eca0626eb72b7129e62023-12-14T00:01:59ZengIEEEIEEE Access2169-35362023-01-011113590513591410.1109/ACCESS.2023.333799210335657Infant Cry Detection With Lightweight Wavelet Scattering NetworksHaitao Cao0https://orcid.org/0000-0002-8626-8686Haishan Chen1https://orcid.org/0000-0003-1759-4004Junying Yuan2https://orcid.org/0000-0003-0156-9373School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, ChinaSchool of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, ChinaSchool of Electrical and Computer Engineering, Guangzhou Nanfang College, Guangzhou, ChinaDevices equipped with algorithms for detecting infant cries allow parents to respond immediately to their crying babies. However, improving detection performance often requires complex algorithms that consume more computing resources, leading to increased power consumption and prices. In this study, we propose a novel approach that leverages first-order Wavelet Scattering Coefficients (WSC) as translation-invariant and deformation-stable representations of infant crying sounds. Based on this approach, we introduce an end-to-end Deep Neural Network (DNN) architecture designed to detect crying using merely 17K parameters and 22.7M MACs. The accuracy results, with a 96.98% accuracy rate on open-source datasets, demonstrate the effectiveness and robustness of our model for detecting infant cries in real-world environments.https://ieeexplore.ieee.org/document/10335657/Infant cry detectionwavelet scatteringlightweight neural networks |
| spellingShingle | Haitao Cao Haishan Chen Junying Yuan Infant Cry Detection With Lightweight Wavelet Scattering Networks IEEE Access Infant cry detection wavelet scattering lightweight neural networks |
| title | Infant Cry Detection With Lightweight Wavelet Scattering Networks |
| title_full | Infant Cry Detection With Lightweight Wavelet Scattering Networks |
| title_fullStr | Infant Cry Detection With Lightweight Wavelet Scattering Networks |
| title_full_unstemmed | Infant Cry Detection With Lightweight Wavelet Scattering Networks |
| title_short | Infant Cry Detection With Lightweight Wavelet Scattering Networks |
| title_sort | infant cry detection with lightweight wavelet scattering networks |
| topic | Infant cry detection wavelet scattering lightweight neural networks |
| url | https://ieeexplore.ieee.org/document/10335657/ |
| work_keys_str_mv | AT haitaocao infantcrydetectionwithlightweightwaveletscatteringnetworks AT haishanchen infantcrydetectionwithlightweightwaveletscatteringnetworks AT junyingyuan infantcrydetectionwithlightweightwaveletscatteringnetworks |