密集连接的网络具有多种功能,用于将声信号与反响分类
Zhuo Chen1, Dazhi Gao1, Kai Sun1
1Department of Marine Technology, Ocean University of China, Qingdao 266100, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种轻量级的DenseNet模型,用于在反响室内环境中准确的声音分类. 优化的模型达到95.90%的准确性,适合在移动设备上部署.
科学领域:
- 声学和信号处理
- 机器学习和人工智能的人工智能
背景情况:
- 室内环境中的反响显著降低了用于主动消除噪声和声音分类的信号质量.
- 现有的方法在杂的反响条件下难以保持准确性.
研究的目的:
- 开发一种强大而轻量级的声音分类模型,能够处理反响效应.
- 为了提高音乐,歌曲和语音信号在具有挑战性的室内声学区分的准确性和效率.
主要方法:
- 由于其轻量特性,使用了DenseNet架构.
- 在不同频率尺度的三个语音光谱特征被融合在一起.
- 使用实验和模拟方法创建了一个数据集,用于培训和验证.
主要成果:
- 合并的DenseNet模型实现了95.90%的分类准确度.
- 这种性能超过了其他卷积神经网络 (CNN) 方法.
- 优化的DenseNet模型大小被缩小到3.09 MB (原来的7.76%).
结论:
- 拟议的基于DenseNet的方法有效地对反响的室内环境中的声音进行分类.
- 这种轻量级模型在Android平台上展示了高效的部署和更快的歧视.
- 这种方法为嵌入式设备上的实时声音分类提供了可行的解决方案.
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