层次-连锁融合TDNN用于声音事件分类
1School of Information Science and Engineering, Shenyang University of Technology, Shenyang City, Liaoning Province, China.
PloS one
|October 31, 2024
概括
一个新的层次连锁融合时间延迟神经网络 (HCF-TDNN) 模型提高了声音事件分类的准确性. 该模型增强了特征提取,以便在复杂的声学环境中更好地识别.
科学领域:
- 声学场景分析 声学场景分析
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 语义特征组合是声音事件分类中的一个挑战.
- 复杂环境中的多个声学事件降低了识别率.
研究的目的:
- 提出分层连锁融合时间延迟神经网络 (HCF-TDNN) 模型.
- 通过解决特征组合问题来提高声音事件分类的准确性.
主要方法:
- HCF模块将音频信号转换为时频特征以进行细分.
- 卷积操作提高了细节感知,扩大了接收领域.
- 频道的注意力和高效的特征传播提高了模型的可扩展性.
主要成果:
- 在Urbansound8K数据集上,HCF-TDNN模型实现了95.83%的分类准确性.
- 这与ECAPA-TDNN模型相比,相对改善了大约5%.
结论:
- 拟议的HCF-TDNN模型有效地解决了语义特征组合的挑战.
- 该模型在复杂的声学环境中表现出卓越的性能,用于声音事件分类.
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