基于双路径结构的保留空间线索的实时立体声语音增强
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure
AI总结：
本研究提出一种基于双路径结构的实时立体声语音增强算法，通过声源特定共带增益保留空间线索，结合预训练单耳模型与自适应波束形成，在不同重叠度的混合信号中均实现有效语音增强与空间线索保留。
摘要：
我们提出了一种实时多通道语音增强算法，该算法可保留包含两个语音源的立体声录音的空间线索。考虑到每个声源都有独特的空间信息，我们的方法采用双路径结构，通过应用针对特定声源的共带增益，确保空间线索在增强过程中不受影响。该方法还可无缝集成预训练的单耳语音增强模型，无需针对立体声输入重新训练。\n立体声混合信号中的声源分离通过空间波束形成实现，每个声源的导向矢量利用增强后的输出信号进行自适应更新，从而确保对空间信息的准确追踪。最终的立体声输出通过合并增强后声源的空间声像得到，其效果并不严重依赖波束形成的分离性能。该算法以10毫秒为帧长、40毫秒前瞻量实时运行。评估结果表明，该算法在完全重叠和稀疏重叠的混合信号中，均能有效增强语音并保留空间线索。
英文摘要：
We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source has unique spatial information, our method utilizes a dual-path structure, ensuring the spatial cues remain unaffected during enhancement by applying source-specific common-band gain. This method also seamlessly integrates pretrained monaural speech enhancement, eliminating the need for retraining on stereo inputs. Source separation from stereo mixtures is achieved via spatial beamforming, with the steering vector for each source being adaptively updated using post-enhancement output signal. This ensures accurate tracking of the spatial information. The final stereo output is derived by merging the spatial images of the enhanced sources, with its efficacy not heavily reliant on the separation performance of the beamforming. The algorithm runs in real-time on 10-ms frames with a 40 ms of look-ahead. Evaluations reveal its effectiveness in enhancing speech and preserving spatial cues in both fully and sparsely overlapped mixtures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
