协调意识的三路卷积循环网络用于唱歌声音分离
Yih-Liang Shen1, Ya-Ching Lai1, Tai-Shih Chi1
1Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu City, Taiwanyihliang.ee06@nycu.edu.tw; r7.ee08@nycu.edu.tw; tschi@nycu.edu.tw.
JASA express letters
|July 5, 2023
概括
一个新的和感知三路卷积循环网络模型通过添加带间循环神经网络 (RNN) 来增强声音分离. 这种先进的模型改进了现有的双路径方法,提高了公共数据集的性能.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 听觉感知是一种听觉感知.
背景情况:
- 听觉流动依赖于时间连贯性和光谱规律性.
- 现有的声音分离模型,如Conv-tasnet和DPCRN使用这些线索.
- DPCRN使用双重循环神经网络分析时间和光谱模式.
研究的目的:
- 通过扩展DPCRN架构,提出一个增强的声音分离模型.
- 调查跨频段循环神经网络对分离性能的影响.
主要方法:
- 开发了一种对意识的三路卷积循环网络模型.
- 在DPCRN框架中整合了一个跨频段的循环神经网络.
- 评估了用于声音分离任务的公共数据集模型.
主要成果:
- 添加带间RNN显著提高了DPCRN模型的分离性能.
- 波感知三路模型在声音分离方面展示了增强的功能.
- 性能增长在已建立的公共数据集上得到了验证.
结论:
- 拟议的和感知三路卷积循环网络模型提供了卓越的声音分离.
- 整合一个带间RNN是提高DPCRN性能的一个有效策略.
- 这项研究有助于在听觉信号处理和用于声音分离的机器学习方面取得进展.
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