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EnglishIn this paper, we address the problem of the concurrent detection of multiple infant cries by using microphones located in the cribs of a Neonatal Intensive Care Unit (NICU). We term this task as infant cry diarization in resemblance with the 'speaker diarization' task related to the speech signal: instead of determining 'who spoke when', here the problem is determining 'who cried when'. The proposed algorithm consists of a fully-convolutional neural network (Conv-DetNet) that processes simultaneously all the audio signals acquired from the microphone in each crib and detects if the infants cried or not. The neural network takes as input Log-Mel coefficients and it is composed of stacked dilated convolutional blocks with increasing dilation factors. Each block is composed of pointwise and depthwise convolutional layers that replace standard convolutions with a mathematically equivalent but more efficient operation. The architecture has been compared to its single-channel equivalent and to single and multi-channel architectures presented in a previous work, composed of standard convolutional layers and fully-connected layers. The experiments have been conducted on a synthetic dataset that simulates the acoustic environment of the Salesi Hospital NICU located in Ancona (Italy). The results have been evaluated in terms of Area Under Precision-Recall Curve (PRC-AUC) and they showed that the proposed multi-channel Conv-DetNet achieves the highest performance with a PRC-AUC equal to 87.58%, outperforming all the comparative methods.
| Titolo: | Who Cried When: Infant Cry Diarization with Dilated Fully-Convolutional Neural Networks | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | In this paper, we address the problem of the concurrent detection of multiple infant cries by using microphones located in the cribs of a Neonatal Intensive Care Unit (NICU). We term this task as infant cry diarization in resemblance with the 'speaker diarization' task related to the speech signal: instead of determining 'who spoke when', here the problem is determining 'who cried when'. The proposed algorithm consists of a fully-convolutional neural network (Conv-DetNet) that processes simultaneously all the audio signals acquired from the microphone in each crib and detects if the infants cried or not. The neural network takes as input Log-Mel coefficients and it is composed of stacked dilated convolutional blocks with increasing dilation factors. Each block is composed of pointwise and depthwise convolutional layers that replace standard convolutions with a mathematically equivalent but more efficient operation. The architecture has been compared to its single-channel equivalent and to single and multi-channel architectures presented in a previous work, composed of standard convolutional layers and fully-connected layers. The experiments have been conducted on a synthetic dataset that simulates the acoustic environment of the Salesi Hospital NICU located in Ancona (Italy). The results have been evaluated in terms of Area Under Precision-Recall Curve (PRC-AUC) and they showed that the proposed multi-channel Conv-DetNet achieves the highest performance with a PRC-AUC equal to 87.58%, outperforming all the comparative methods. | |
| Handle: | http://hdl.handle.net/11566/286031 | |
| ISBN: | 978-1-7281-6926-2 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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