强大的框架级扬声器本地化由多通道语音增强和通道间相差损失指导
Shanmukha Srinivas Battula1, Hassan Taherian1, Ashutosh Pandey2
1Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
The Journal of the Acoustical Society of America
|July 28, 2025
概括
这项研究使用复杂的光谱映射 (CSM) 来增强语音和到达方向 (DOA) 估计,在杂的房间中增强扬声器定位. 具有新损失功能的多输入多输出 (MIMO) 系统显著提高了比单输出系统的准确性.
科学领域:
- 信号处理 信号处理
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 框架级扬声器定位在反响和噪音环境中具有挑战性.
- 现有的深度学习方法经常直接处理多通道音频,从而限制了性能.
- 精确的声音源定位依赖于保留通道间的相位信息.
研究的目的:
- 开发一个强大的框架级扬声器定位方法,以适应不利的声学条件.
- 研究使用复杂光谱映射 (CSM) 进行多通道语音增强,以改进到达方向 (DOA) 估计.
- 提出和评估新的多输入多输出 (MIMO) 系统和损失函数,以提高本地化准确性.
主要方法:
- 使用复杂光谱映射 (CSM) 进行多通道语音增强.
- 通过加权的一般化与相变换 (GCC-PHAT) 交叉相关的到达方向 (DOA) 估计.
- 多输入单输出 (MISO) 和多输入多输出 (MIMO) 模型的比较,包括包含相位差异的新损失函数.
主要成果:
- 从CSM衍生的阶段估计对于框架级DOA估计是可靠的.
- 与MISO系统相比,MIMO系统表现出优越的性能.
- 拟议的模型实现了优秀的级扬声器本地化,显著优于相关方法.
结论:
- 拟议的基于CSM的语音增强和DOA估计框架有效地解决了噪音和反响环境中的限制.
- 具有新的相位感知损失功能的MIMO系统在扬声器定位准确性方面取得了显著的改进.
- 该方法实现了最先进的框架级本地化性能,甚至超过了其他方法的发言级结果.
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