StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation
This provides a more accessible and effective tool for audio data augmentation in machine learning applications, though it is incremental as it builds on existing RIR generation concepts.
The authors tackled the problem of generating room impulse responses (RIRs) for audio data augmentation without needing detailed room geometry, and showed that using their StoRIR method improved speech enhancement model performance by over 5% on various metrics compared to conventional methods.
In this paper we introduce StoRIR - a stochastic room impulse response generation method dedicated to audio data augmentation in machine learning applications. This technique, in contrary to geometrical methods like image-source or ray tracing, does not require prior definition of room geometry, absorption coefficients or microphone and source placement and is dependent solely on the acoustic parameters of the room. The method is intuitive, easy to implement and allows to generate RIRs of very complicated enclosures. We show that StoRIR, when used for audio data augmentation in a speech enhancement task, allows deep learning models to achieve better results on a wide range of metrics than when using the conventional image-source method, effectively improving many of them by more than 5 %. We publish a Python implementation of StoRIR online