Sound event detection based on multi-channel multi-scale neural networks for home monitoring system used by the hard-of-hearing

Gi Yong Lee and Hyoung-Gook Kim
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Abstract

: In this paper, we propose a sound event detection method using a multi-channel multi-scale neural networks for sound sensing home monitoring for the hearing impaired. In the proposed system, two channels with high signal quality are selected from several wireless microphone sensors in home. The three features (time difference of arrival, pitch range, and outputs obtained by applying multi-scale convolutional neural network to log mel spectrogram) extracted from the sensor signals are applied to a classifier based on a bidirectional gated recurrent neural network to further improve the performance of sound event detection. The detected sound event result is converted into text along with the sensor position of the selected channel and provided to the hearing impaired. The experimental results show that the sound event detection method of the proposed system is superior to the existing method and can effectively deliver sound information to the hearing impaired
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基于多通道多尺度神经网络的听力困难家庭监测系统声事件检测
:在本文中,我们提出了一种使用多通道多尺度神经网络的声音事件检测方法,用于听力受损者的声音传感家庭监测。在所提出的系统中,从家中的几个无线麦克风传感器中选择两个具有高信号质量的通道。从传感器信号中提取的三个特征(到达时间差、音高范围和通过将多尺度卷积神经网络应用于对数mel频谱图获得的输出)被应用于基于双向门控递归神经网络的分类器,以进一步提高声音事件检测的性能。检测到的声音事件结果与所选通道的传感器位置一起被转换成文本,并被提供给听力受损者。实验结果表明,该系统的声音事件检测方法优于现有方法,能够有效地向听力受损者传递声音信息
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CiteScore
0.60
自引率
50.00%
发文量
1
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