RIR-Mega: a large-scale simulated room impulse response dataset for
  machine learning and room acoustics modeling
Room impulse responses are a core resource for dereverberation, robust speech
recognition, source localization, and room acoustics estimation. We present
RIR-Mega, a large collection of simulated RIRs described by a compact, machine
friendly metadata schema and distributed with simple tools for validation and
reuse. The dataset ships with a Hugging Face Datasets loader, scripts for
metadata checks and checksums, and a reference regression baseline that
predicts RT60 like targets from waveforms. On a train and validation split of
36,000 and 4,000 examples, a small Random Forest on lightweight time and
spectral features reaches a mean absolute error near 0.013 s and a root mean
square error near 0.022 s. We host a subset with 1,000 linear array RIRs and
3,000 circular array RIRs on Hugging Face for streaming and quick tests, and
preserve the complete 50,000 RIR archive on Zenodo. The dataset and code are
public to support reproducible studies.
68f980bcb9b2e4ae04673722
Mandip Goswami
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2025-10-23T14:56:42.707Z
67d43aeb6ffb8add49ea6712
https://cdn-avatars.huggingface.co/v1/production/uploads/67d43aeb6ffb8add49ea6712/0h4bV3Ptgh_zTRUHVqwe2.jpeg
Mandip Goswami
user
mandipgoswami
RIR-Mega: a large-scale simulated room impulse response dataset for
  machine learning and room acoustics modeling
Room impulse responses are a core resource for dereverberation, robust speech
recognition, source localization, and room acoustics estimation. We present
RIR-Mega, a large collection of simulated RIRs described by a compact, machine
friendly metadata schema and distributed with simple tools for validation and
reuse. The dataset ships with a Hugging Face Datasets loader, scripts for
metadata checks and checksums, and a reference regression baseline that
predicts RT60 like targets from waveforms. On a train and validation split of
36,000 and 4,000 examples, a small Random Forest on lightweight time and
spectral features reaches a mean absolute error near 0.013 s and a root mean
square error near 0.022 s. We host a subset with 1,000 linear array RIRs and
3,000 circular array RIRs on Hugging Face for streaming and quick tests, and
preserve the complete 50,000 RIR archive on Zenodo. The dataset and code are
public to support reproducible studies.
RIR-Mega is a large dataset of simulated room impulse responses with tools for validation and a baseline model for predicting RT60 from waveforms.
Hugging Face Datasets Random Forest time features spectral features RT60 room impulse responses