Pyroomacoustics: A Python Package for Audio Room Simulation and Array Processing Algorithms

By Robin Scheibler, Eric Bezzam, Ivan Dokmanić. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017

687 citations recorded by the source corpus.

We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the package can be divided into three main components: an intuitive Python object-oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms; a fast C implementation of the image source model for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers; and finally, reference implementations of popular algorithms for beamforming, direction finding, and adaptive filtering. Together, they form a package with the potential to speed up the time to market of new algorithms by significantly reducing the implementation overhead in the performance evaluation step.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/331029aef0e1024fb1865a30e173dffdb2d18730

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Citations in the corpus

Speech Enhancement and Dereverberation With Diffusion-Based Generative Models (2022, methodology): “We use the pyroomacoustics engine [53] to simulate the RIRs.”
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition (2019, methodology): “We make initial steps towards developing audio which can be played over-the-air by designing audio which remains adversarial after being processed by random roomenvironment simulators (Scheibler et al., 2018).”
WHAMR!: Noisy and Reverberant Single-Channel Speech Separation (2019, methodology): “Room impulse responses were generated and convolved using pyroomacoustics [14] according to the random room conﬁgurations shown in Table 1.”
Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net (2020, methodology): “Since our goal is to perform both denoising and dereverberation, we used pyroomacoustics [26] to simulate an artiﬁcial reverberation with randomly sampled absorption, room size, location of source and microphone distance.”