# Soundscapy

> Part of [LISQM](https://zhenxianli.github.io/LISQM/) (List of Implementations of Sound Quality Metrics), a list of open-source implementations of psychoacoustic metrics. Data as of 2026-10-28. Web page: https://zhenxianli.github.io/LISQM/projects/soundscapy.html

Python library for soundscape assessment. Its optional audio module computes psychoacoustic metrics of binaural recordings, per channel, by calling MoSQITo: time-varying Zwicker loudness, DIN 45692 sharpness and Daniel & Weber roughness.

- **Repository:** https://github.com/MitchellAcoustics/Soundscapy
- **Documentation:** https://soundscapy.readthedocs.io/en/latest/
- **Language:** Python
- **Kind:** Library/Toolbox (Functions to call from your own code: a Python package, a MATLAB toolbox, or a C, C++, C#, Rust or Julia library.)
- **Group:** other (Tools that do not compute the metrics themselves: interfaces, front ends and wrappers that call one of the listed projects.)
- **Licence:** BSD-3-Clause
- **Package:** PyPI `soundscapy` (https://pypi.org/project/soundscapy/)
- **Install:** `pip install "soundscapy[audio]"`
- **Latest release:** v0.8.5 (2026-05)
- **Last commit:** 2026-05-14 (active)
- **GitHub stars:** 59
- **Maintainers:** Andrew Mitchell
- **AI assistance:** disclosed — Commit metadata only; the README does not mention AI. At least 31 commits from March to May 2026 carry co-author lines for Claude or Copilot, and eight commits are authored by copilot-swe-agent[bot].
- **Paper:** Mitchell, A., Aletta, F., & Kang, J. (2022). How to analyse and represent quantitative soundscape data. *JASA Express Letters*, 2, 037201. https://doi.org/10.1121/10.0009794
- **How to cite:** Cite the paper above. The repository has a [CITATION.cff](https://github.com/MitchellAcoustics/Soundscapy/blob/main/CITATION.cff) file. Name the version or commit you used.
- **Entry checked:** 2026-10-06

## What it implements

| Metric | Edition | Functions | Status | Validation (as stated) | Notes |
|---|---|---|---|---|---|
| [Zwicker loudness](https://zhenxianli.github.io/LISQM/metrics/loudness-zwicker.html) | [ISO 532-1:2017](https://www.iso.org/standard/63077.html) (time-varying, per channel) | `Binaural.mosqito_metric`, `process_all_metrics` | available | not stated | Calls `loudness_zwtv`. |
| [Sharpness](https://zhenxianli.github.io/LISQM/metrics/sharpness.html) | [DIN 45692:2009](https://www.dinmedia.de/en/standard/din-45692/117635111) | `Binaural.mosqito_metric`, `process_all_metrics` | available | not stated | Calls `sharpness_din_from_loudness`, `sharpness_din_perseg` or `sharpness_din_tv`; the weighting can be chosen. |
| [Roughness (Daniel & Weber)](https://zhenxianli.github.io/LISQM/metrics/roughness-daniel-weber.html) | [Daniel & Weber (1997)](https://zhenxianli.github.io/LISQM/standards.html#ref-daniel-weber-1997) | `Binaural.mosqito_metric`, `process_all_metrics` | available | not stated | Calls `roughness_dw`. |

## How it was validated

As reported by the project; LISQM has not run the code. Two implementations that agree can still share the same error.

The project does not say how any of these were validated.

## Notes

- The psychoacoustic functions need the optional `audio` extra (MoSQITo 1.2.1 or newer). Environmental acoustic metrics come from acoustic-toolbox and ecoacoustic indices from scikit-maad.
- Issue [#123](https://github.com/MitchellAcoustics/Soundscapy/issues/123), open since 2025-10-29, plans to integrate sottek-hearing-model for ECMA-418-2 metrics; its branch has no such code yet.
- The paper the README asks users to cite covers soundscape data analysis, not the psychoacoustic functions.

## Before you rely on it

- The psychoacoustic values are computed by MoSQITo, so their editions and accuracy are those of the installed MoSQITo version.

## Sources

- https://github.com/MitchellAcoustics/Soundscapy
- https://github.com/MitchellAcoustics/Soundscapy/blob/main/README.md
- https://github.com/MitchellAcoustics/Soundscapy/blob/main/src/soundscapy/audio/metrics.py
- https://github.com/MitchellAcoustics/Soundscapy/issues/123
- https://pypi.org/project/soundscapy/