基于时间-频率-波段融合网络的声学场景分类研究
Fengzheng Bi1, Lidong Yang1,2
1School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种用于声场景分类的新型时频波小组融合网络. 拟议的模型显著提高了城市声音数据集的准确性,证明了其在复杂的声学环境中的有效性.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 声学场景分类从声音信号识别环境.
- 由于不同城市和设备的音频变化,挑战了模型的准确性.
- 现有的方法与多样化和复杂的声学环境作斗争.
研究的目的:
- 开发一个先进的声学场景分类模型.
- 为了增强对音频变化的模型稳定性.
- 在现实场景中提高分类准确性.
主要方法:
- 一个时频波小组聚变网络被提出.
- 一个时间-频率-波形模块提取了跨时间,频率和波形域的特征.
- 封闭的时空注意力和视觉状态空间模块被整合为增强的上下文建模.
- 科尔摩戈罗夫-阿诺德网络层在分类器中取代了传统的多层感知子.
主要成果:
- 在TAU城市声学场景2022移动数据集上实现了56.16%的平均准确性,比基线提高了6.53%.
- 在UrbanSound8K数据集上达到97.60%的准确性,优于现有方法.
- 在复杂的声学场景中表现出显著的性能提升.
结论:
- 拟议的时间-频率-波纹融合网络有效地提高了声场景分类的准确性.
- 该模型在不同数据集和复杂环境中表现出强大的概括能力.
- 多维音频信息的融合对于强大的声学场景识别至关重要.
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