Simulated Room Impulse Response for 44.1k Audio

We randomly simulated a collection of Room Impulse Response filters to simulate the 44.1kHz speech room reverberation using an open-source tool (https://github.com/sunits/rir_simulator_python). The meters of height, width, and length of the room are sampled randomly in a uniform distribution U(1,12). The placement of the microphone is then randomly selected within the room space. For the placement of the sound source, we first determined the distance between the microphone and sound source, which is randomly sampled in a Gaussian distribution N(\mu,\sigma^2), \mu=2, \sigma=4. If the sampled value is negative or greater than five meters, we will sample the distance again until it meets the requirement. After determined the distance between the microphone and sound source, the placement of the sound source is randomly selected on the sphere centered at the microphone. The RT60 value we choose comes from the uniform distribution U(0.05,1.0). For the pickup pattern of the microphone, we randomly choose from types omnidirectional and cardioid. If you found this dataset helpful, please consider citing: @article{liu2021voicefixer,
title={VoiceFixer: Toward General Speech Restoration with Neural Vocoder},
author={Liu, Haohe and Kong, Qiuqiang and Tian, Qiao and Zhao, Yan and Wang, DeLiang and Huang, Chuanzeng and Wang, Yuxuan},
journal={arXiv preprint arXiv:2109.13731},
year={2021}
}

我们借助开源工具（https://github.com/sunits/rir_simulator_python）随机生成了一组房间脉冲响应（Room Impulse Response, RIR）滤波器，以模拟44.1kHz频段的语音房间混响效果。房间的长、宽、高尺寸均服从均匀分布U(1,12)进行随机采样。随后，麦克风的摆放位置在房间空间内随机选取。对于声源的摆放，我们首先确定麦克风与声源之间的距离：该距离服从高斯分布N(μ,σ²)，其中μ=2，σ=4；若采样得到的距离为负值或大于5米，则重新进行采样，直至满足要求。确定麦克风与声源的间距后，再以麦克风为中心，在对应的球面上随机选取声源的摆放位置。我们选取的RT60参数服从均匀分布U(0.05,1.0)。麦克风的拾音模式则从全指向（omnidirectional）和心形指向（cardioid）两种类型中随机选择。若您认为本数据集对研究有所助益，请引用以下文献：
@article{liu2021voicefixer,
title={VoiceFixer: Toward General Speech Restoration with Neural Vocoder},
author={Liu, Haohe and Kong, Qiuqiang and Tian, Qiao and Zhao, Yan and Wang, DeLiang and Huang, Chuanzeng and Wang, Yuxuan},
journal={arXiv preprint arXiv:2109.13731},
year={2021}
}