WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement

This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER Kiselev2018. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). === Each sample is a 16-channel wav file in which the order of the channel follows the following logic: 0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4 Refer to the floor plan for a visual illustration of the microphone arrangement. The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation. In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4) In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB) Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav). == A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on this beamforming library and this tool to evaluate the quality of the separation. == Refer to the README.md in the dataset for more information. For any question please contact [e-mail]本数据集为<code>WHISPER SET 1</code>，是一款用于语音增强与语音源分离的数据集，由名为WHISPER的无线声学传感器网络（Wireless Acoustic Sensor Network, WASN）采集所得，相关研究标注为Kiselev2018。该数据集包含多达4路并发讲话者的语音以及带噪语音样本，采集场景为混响时间T_60=0.2秒的低混响房间，共使用16路麦克风。 通常情况下，每条音轨首先包含一段校准阶段：每位讲话者依次单独发声15秒，随后为15秒的多讲话者同步发声场景（部分场景下会叠加背景噪声）。 === 每个样本为16通道的WAV文件，其声道排布逻辑如下： 0 - 模组5麦克风1 1 - 模组5麦克风2 2 - 模组5麦克风3 3 - 模组5麦克风4 4 - 模组6麦克风1 5 - 模组6麦克风2 6 - 模组6麦克风3 7 - 模组6麦克风4 8 - 模组7麦克风1 9 - 模组7麦克风2 10 - 模组7麦克风3 11 - 模组7麦克风4 12 - 模组8麦克风1 13 - 模组8麦克风2 14 - 模组8麦克风3 15 - 模组8麦克风4 麦克风阵列的具体布局可参考平面布置图。 数据集文件分为两个子文件夹，分别对应语音增强任务样本与语音分离任务样本。 在语音分离任务子文件夹中，样本按混合语音的讲话者数量（2、3或4）划分为不同子文件夹；在语音增强任务子文件夹中，样本按混合语音的信噪比（Signal-to-Noise Ratio, SNR）划分为不同子文件夹，涵盖0、-5、-10 dB三种场景。 样本均以文件夹组织。每个样本文件夹均包含一段15秒的16通道<code>mixture.wav</code>混合音频文件，以及对应数量的15秒16通道<code>calibX.wav</code>校准文件，每个校准文件对应混合场景中的一位单独讲话者或单独背景噪声。例如，包含4位讲话者的混合样本将附带calib1.wav、calib2.wav、calib3.wav、calib4.wav共4个校准文件；而包含单讲话者加背景噪声的混合样本则附带2个校准文件，分别为语音校准文件calib1.wav与噪声校准文件calib2.wav。 == 本数据集附带一份Jupyter Notebook，演示如何结合波束成形（beamforming）技术，使用该数据集的音频数据完成语音分离与语音增强任务。该Notebook依赖指定的波束成形库与语音分离质量评估工具方可运行。 更多详细信息请参阅数据集根目录下的README.md文件。如有任何疑问，请联系邮箱[e-mail]。