# LCAV/pyroomacoustics

Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.

Repository: https://github.com/LCAV/pyroomacoustics
Canonical: https://ross.abutalabs.com/products/pyroomacoustics
Homepage: https://pyroomacoustics.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: acoustics, room-impulse-response, image-source-model, beamforming, doa, adaptive-filtering, stft, audio
Last push: 2026-07-17T01:55:51+00:00

## Health v2 (maintenance only)
Score: 90/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 92, release rhythm 81, longevity 100
- inputs: {"age_days": 3905, "days_push": 48, "days_rel": 124, "gap_med": 0, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1928, forks 496 (observed 2026-08-28T04:05:55.928790+00:00)

## What it is
Pyroomacoustics is a Python package for audio signal processing in indoor scenarios, combining a fast C++ room acoustics simulator (image source model and ray tracing) with reference implementations of array processing algorithms. It provides an object-oriented interface for simulating sound sources and microphone arrays in 2D/3D rooms and generating room impulse responses.

## Use cases
- simulate room impulse responses for a microphone array in a reverberant room
- prototype and benchmark beamforming algorithms
- estimate direction of arrival of sound sources
- generate training data for machine learning audio models
- test adaptive filtering and source separation methods
- simulate acoustic propagation between sources and receivers in 2D and 3D rooms
- denoise single-channel speech recordings

## When to choose
- you need to simulate room acoustics or generate room impulse responses quickly
- you are developing or evaluating microphone array processing algorithms like beamforming or DOA estimation
- you want reference Python implementations of STFT, adaptive filtering, or denoising algorithms
- you need synthetic reverberant audio data for research or ML training

## When to avoid
- you need real-time audio processing in production systems
- you require physically accurate auralization for professional acoustic engineering or architectural design
- you need full 3D spatial audio rendering for games or VR
- you want a GUI-based acoustic simulation tool rather than a Python library

## Facets
- artifact type: library
- maturity: stable
- function: audio-processing, simulation, nlp, machine-learning
- domain: speech-processing
- platform: python, cross-platform
- tags: room-acoustics, room-impulse-response, image-source-model, beamforming, direction-of-arrival, doa-estimation, microphone-arrays, adaptive-filtering, stft, source-separation, speech-enhancement, ray-tracing, audio, algorithms, research, signal-processing

## Member repositories
- LCAV/pyroomacoustics (main) score 90

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:55.928790+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:09:06.525446+00:00, confidence not recorded.
  - readme: https://github.com/LCAV/pyroomacoustics (fetched 2026-08-28T04:05:55.928790+00:00, sha 35f3bdf8c4ee)
  - registry_pypi: https://pypi.org/pypi/pyroomacoustics/json (fetched 2026-08-29T10:48:59.064038+00:00, sha 35f86ebd433a)
- Data as of 2026-08-30T08:39:29.467469+00:00.