RealMAN：一个用于动态语音增强和定位的真实录制且带标注的麦克风阵列数据集
RealMAN: A Real-Recorded and Annotated Microphone Array Dataset for Dynamic Speech Enhancement and Localization
AI总结：
本文提出RealMAN数据集，包含32通道真实录制的83.7小时语音和144.5小时噪声，覆盖多种场景，并利用鱼眼相机自动标注声源位置和直达路径信号，以弥合语音增强与定位任务中仿真到真实的差距。
摘要：
由于缺乏大规模真实录制的数据集，基于深度学习的多通道语音增强和声源定位系统的训练在很大程度上依赖于房间脉冲响应和多通道扩散噪声的仿真。然而，仿真数据与真实世界数据之间的声学失配可能会在实际场景应用中降低模型性能。为了弥合这一从仿真到真实的差距，本文提出了一个新的相对大规模的真实录制且带标注的麦克风阵列语音与噪声（RealMAN）数据集。所提出的数据集在两个方面具有价值：1）在真实场景中对语音增强和定位算法进行基准测试；2）提供大量真实世界的训练数据，以潜在地提升真实世界应用的性能。具体而言，使用了一个配备高保真麦克风的32通道阵列进行录制。使用一个扬声器播放源语音信号（约35小时普通话语音）。在32个不同场景中总共录制了83.7小时的语音信号（约48.3小时为静态说话人，35.4小时为移动说话人），并在31个不同场景中录制了144.5小时的背景噪声。语音和噪声录制场景涵盖了各种常见的室内、室外、半室外和交通环境，这使得能够训练通用语音增强和声源定位网络。为了获得任务特定的标注，使用全向鱼眼相机通过自动检测扬声器来标注说话人位置。直达路径信号被设定为语音增强的目标干净语音，该信号通过使用估计的直达路径传播滤波器对源语音信号进行滤波而获得。
英文摘要：
The training of deep learning-based multichannel speech enhancement and source localization systems relies heavily on the simulation of room impulse response and multichannel diffuse noise, due to the lack of large-scale real-recorded datasets. However, the acoustic mismatch between simulated and real-world data could degrade the model performance when applying in real-world scenarios. To bridge this simulation-to-real gap, this paper presents a new relatively large-scale Real-recorded and annotated Microphone Array speech&Noise (RealMAN) dataset. The proposed dataset is valuable in two aspects: 1) benchmarking speech enhancement and localization algorithms in real scenarios; 2) offering a substantial amount of real-world training data for potentially improving the performance of real-world applications. Specifically, a 32-channel array with high-fidelity microphones is used for recording. A loudspeaker is used for playing source speech signals (about 35 hours of Mandarin speech). A total of 83.7 hours of speech signals (about 48.3 hours for static speaker and 35.4 hours for moving speaker) are recorded in 32 different scenes, and 144.5 hours of background noise are recorded in 31 different scenes. Both speech and noise recording scenes cover various common indoor, outdoor, semi-outdoor and transportation environments, which enables the training of general-purpose speech enhancement and source localization networks. To obtain the task-specific annotations, speaker location is annotated with an omni-directional fisheye camera by automatically detecting the loudspeaker. The direct-path signal is set as the target clean speech for speech enhancement, which is obtained by filtering the source speech signal with an estimated direct-path propagation filter.
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。
