Lung Sound Recognition Algorithm Based on VGGish-BiGRU
| Autor: | Chaozong Zhang, Kang Du, Hongqi Ma, Lukui Shi, Wenjie Yan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Speech recognition 0206 medical engineering Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology transfer learning Convolutional neural network lung sound recognition 03 medical and health sciences otorhinolaryngologic diseases General Materials Science BiGRU Mel spectrogram 030304 developmental biology Sound (medical instrument) 0303 health sciences Artificial neural network General Engineering Network layer 020601 biomedical engineering respiratory tract diseases Support vector machine ComputingMethodologies_PATTERNRECOGNITION VGGish Key (cryptography) lcsh:Electrical engineering. Electronics. Nuclear engineering Transfer of learning lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 139438-139449 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Pulmonary breathing sound plays a key role in the prevention and diagnosis of the lung diseases. Its correlation with pathology and physiology has become an important research topic in the pulmonary acoustics and the clinical medicine. However, it is difficult to fully describe lung sound information with the traditional features because lung sounds are complex and nonstationary signals. And the traditional convolutional neural network cannot also extract the temporal features of the lung sounds. To solve the problem, a lung sound recognition algorithm based on VGGish-BiGRU is proposed on the basis of transfer learning, which combines VGGish network with the bidirectional gated recurrent unit neural network (BiGRU). In the proposed algorithm, VGGish network is pretrained using audio set, and the parameters are transferred to VGGish network layer of the target network. The temporal features of the lung sounds are extracted through retraining BiGRU network with the lung sound data. During retraining BiGRU network, the parameters in VGGish layers are frozen, and the parameters of BiGRU network are fine-tuned. The experimental results show that the proposed algorithm effectively improves the recognition accuracy of the lung sounds in contrast with the state-of-the-art algorithms, especially the recognition accuracy of asthma. |
| Databáze: | OpenAIRE |
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