# LISQM: List of Implementations of Sound Quality Metrics

> A list of open-source implementations of psychoacoustic metrics, also called sound quality (SQ) metrics: loudness, sharpness, roughness, fluctuation strength, tonality, impulsiveness and psychoacoustic annoyance. Each implementation is listed under the standard edition or model it follows. All programming languages.

Data as of 2026-10-28. 22 metrics, 41 projects, languages: C, C#, C++, Julia, MATLAB, Octave, Pure Data, Python, Rust. Each implementation is tied to the standard edition or model paper it follows, with its validation evidence as stated by the project. Repository and package metadata are refreshed monthly. Implementations are listed with established projects first, SQAT and MoSQITo (widely used), and sottek-hearing-model (a very good implementation) at the top and in bold. Projects marked (newly released) were first released less than about a year ago and are not yet seen to be widely used in the community; projects marked (legacy) are archived or have had no commit for three years or more.

Developed by Zhenxian LI with assistance from Claude Code.

## Loudness

- [Zwicker loudness (ISO 532-1, DIN 45631)](https://zhenxianli.github.io/LISQM/metrics/loudness-zwicker.md): current edition ISO 532-1:2017; implementations: SQAT, MoSQITo, AARAE, MetaSona (newly released), iso532-1-rs (newly released), Kirin Hypha (newly released), Mosqito.NET (newly released), phonometry (newly released), SoundPalette (newly released), ZwickerLoudness.jl (newly released), pySQAT (developing), PsychoacousticParametersMeasurer (legacy)
- [Moore–Glasberg loudness of stationary sounds (ISO 532-2, ANSI S3.4)](https://zhenxianli.github.io/LISQM/metrics/loudness-moore-glasberg.md): current edition ISO 532-2:2017; implementations: phonometry (newly released)
- [Time-varying loudness, Moore–Glasberg–Schlittenlacher (ISO 532-3)](https://zhenxianli.github.io/LISQM/metrics/loudness-moore-glasberg-time-varying.md): current edition ISO 532-3:2023; implementations: iso532-3 (tv2018.m), phonometry (newly released)
- [Loudness from the Sottek Hearing Model (ECMA-418-2)](https://zhenxianli.github.io/LISQM/metrics/loudness-ecma-418-2.md): current edition ECMA-418-2:2025 (4th ed.); implementations: SQAT, sottek-hearing-model, refmap-psychoacoustics, MetaSona (newly released), phonometry (newly released)

## Sharpness

- [Sharpness (DIN 45692, Aures, von Bismarck)](https://zhenxianli.github.io/LISQM/metrics/sharpness.md): current edition DIN 45692:2009; implementations: SQAT, MoSQITo, refmap-psychoacoustics, MetaSona (newly released), Kirin Hypha (newly released), Mosqito.NET (newly released), phonometry (newly released), PsychoacousticMetrics.jl (newly released), SoundPalette (newly released), pySQAT (developing), PsychoacousticParametersMeasurer (legacy)

## Roughness

- [Roughness, Daniel & Weber model](https://zhenxianli.github.io/LISQM/metrics/roughness-daniel-weber.md): current edition Daniel & Weber (1997); implementations: SQAT, MoSQITo, AARAE, ITA-Toolbox, MetaSona (newly released), Mosqito.NET (newly released), PsychoacousticMetrics.jl (newly released), SoundPalette (newly released), pySQAT (developing), PsySound3 (legacy)
- [Roughness from the Sottek Hearing Model (ECMA-418-2)](https://zhenxianli.github.io/LISQM/metrics/roughness-ecma-418-2.md): current edition ECMA-418-2:2025 (4th ed.); implementations: SQAT, sottek-hearing-model, refmap-psychoacoustics, MetaSona (newly released), ecma418-torch (newly released), phonometry (newly released)
- [Roughness, DIN 38455](https://zhenxianli.github.io/LISQM/metrics/roughness-din-38455.md): current edition DIN 38455:2024; implementations: none found

## Fluctuation strength

- [Fluctuation strength, Osses et al. model and Fastl & Zwicker formula](https://zhenxianli.github.io/LISQM/metrics/fluctuation-strength-osses.md): current edition Osses et al. (2016); implementations: SQAT, phonometry (newly released), PsychoacousticMetrics.jl (newly released), pySQAT (developing), fluctuation-strength-TUe (legacy)
- [Fluctuation strength from the Sottek Hearing Model (ECMA-418-2)](https://zhenxianli.github.io/LISQM/metrics/fluctuation-strength-ecma-418-2.md): current edition ECMA-418-2:2025 (4th ed.); implementations: SQAT (PR), sottek-hearing-model (unreleased), ecma418-torch (newly released), phonometry (newly released)

## Tonality

- [Tonality, Aures/Terhardt model](https://zhenxianli.github.io/LISQM/metrics/tonality-aures.md): current edition Aures (1985b); implementations: SQAT, MoSQITo (PR), MetaSona (newly released), pySQAT (developing)
- [Tonality from the Sottek Hearing Model (ECMA-418-2)](https://zhenxianli.github.io/LISQM/metrics/tonality-ecma-418-2.md): current edition ECMA-418-2:2025 (4th ed.); implementations: SQAT, sottek-hearing-model, refmap-psychoacoustics, MetaSona (newly released), phonometry (newly released)
- [Tone-to-noise ratio and prominence ratio (ECMA-418-1)](https://zhenxianli.github.io/LISQM/metrics/tone-to-noise-prominence-ratio.md): current edition ECMA-418-1:2024 (3rd ed.); implementations: phonometry (newly released), rota-order (newly released)
- [Tonal components and tone adjustment (DIN 45681)](https://zhenxianli.github.io/LISQM/metrics/tonality-din-45681.md): current edition DIN 45681:2005; implementations: none found
- [Audibility of tones in noise (ISO/TS 20065)](https://zhenxianli.github.io/LISQM/metrics/tonal-audibility-iso-20065.md): current edition ISO/TS 20065:2022; implementations: none found
- [Tonal audibility in environmental noise (ISO 1996-2)](https://zhenxianli.github.io/LISQM/metrics/tonal-audibility-iso-1996-2.md): current edition ISO 1996-2:2017; implementations: MoSQITo (PR), phonometry (newly released)
- [Tonal audibility of wind turbine noise (IEC 61400-11)](https://zhenxianli.github.io/LISQM/metrics/tonal-audibility-iec-61400-11.md): current edition IEC 61400-11:2012+AMD1:2018; implementations: phonometry (newly released)

## Impulsiveness

- [Prominence of impulsive sounds (ISO/PAS 1996-3, NT ACOU 112)](https://zhenxianli.github.io/LISQM/metrics/impulsiveness.md): current edition ISO/PAS 1996-3:2022; implementations: phonometry (newly released)

## Psychoacoustic annoyance

- [Psychoacoustic annoyance (Widmann, Zwicker & Fastl, More, Di et al.)](https://zhenxianli.github.io/LISQM/metrics/psychoacoustic-annoyance.md): current edition Widmann (1992), Zwicker & Fastl (1999), More (2010), and Di et al. (2016); implementations: SQAT, refmap-psychoacoustics, phonometry (newly released), PsychoacousticMetrics.jl (newly released), pySQAT (developing), PsychoacousticParametersMeasurer (legacy)

## Related quantities

- [Equal-loudness-level contours (ISO 226)](https://zhenxianli.github.io/LISQM/metrics/equal-loudness-contours.md): current edition ISO 226:2023; implementations: phonometry (newly released)
- [Perceived noise level, PNLT and EPNL (ICAO Annex 16, 14 CFR Part 36)](https://zhenxianli.github.io/LISQM/metrics/epnl.md): current edition ICAO Annex 16, Vol. I and 14 CFR Part 36; implementations: SQAT, phonometry (newly released), pySQAT (developing), SUAVE (legacy)
- [Aural detectability of sounds in background noise](https://zhenxianli.github.io/LISQM/metrics/aural-detectability.md): current edition Fidell et al. (1974); implementations: refmap-psychoacoustics

## Projects

- **[SQAT](https://zhenxianli.github.io/LISQM/projects/sqat.md)**: MATLAB; established, widely used; MATLAB toolbox for sound quality analysis that bundles most psychoacoustic metrics in one place: ISO 532-1 and ECMA-418-2 loudness, DIN 45692 sharpness, Daniel & Weber and ECMA-418-2 roughness, Osses fluctuation strength, Aures and ECMA-418-2 tonality, three psychoacoustic annoyance models (Widmann's, which SQAT also offers under its common attribution to Zwicker & Fastl; More; Di et al.) and EPNL.
- **[MoSQITo](https://zhenxianli.github.io/LISQM/projects/mosqito.md)**: Python; established, widely used; Python library for sound quality metrics: ISO 532-1 loudness, DIN 45692 sharpness, Daniel & Weber roughness, ECMA-418-2 hearing-model loudness (Clause 5) and roughness (2nd edition), tone-to-noise and prominence ratios, and the speech intelligibility index.
- **[sottek-hearing-model](https://zhenxianli.github.io/LISQM/projects/sottek-hearing-model.md)**: Python; established, a very good implementation; Python implementation of the ECMA-418-2:2025 (4th edition) Sottek Hearing Model metrics: loudness and roughness (with binaural combination), tonality (per channel), and fluctuation strength on the main branch.
- [acoustic-toolbox](https://zhenxianli.github.io/LISQM/projects/acoustic-toolbox.md): Python; established; Python acoustics library under the Université Gustave Eiffel GitHub organisation, continuing python-acoustics.
- [AMT](https://zhenxianli.github.io/LISQM/projects/amt.md): MATLAB, Octave; established; MATLAB/Octave toolbox of auditory models for reproducible research.
- [refmap-psychoacoustics](https://zhenxianli.github.io/LISQM/projects/refmap-psychoacoustics.md): MATLAB, Python; established; Research code of the REFMAP project at the University of Salford, in MATLAB with Python ports.
- [AARAE](https://zhenxianli.github.io/LISQM/projects/aarae.md): MATLAB; established; Audio and Acoustical Response Analysis Environment: a MATLAB environment with a graphical interface, made to support teaching and research in acoustics.
- [iso532-3 (tv2018.m)](https://zhenxianli.github.io/LISQM/projects/js2251-iso532-3.md): MATLAB; established; MATLAB code by Brian C. J. Moore, Brian R. Glasberg and Josef Schlittenlacher (Cambridge Hearing Group) for the loudness of time-varying sounds: the model of Moore et al. (2016) with the revised time constants of Moore et al. (2018).
- [ITA-Toolbox](https://zhenxianli.github.io/LISQM/projects/ita-toolbox.md): MATLAB; established; MATLAB toolbox for acoustic measurements and signal processing from the Institute of Technical Acoustics, RWTH Aachen University, open source since 2010.
- [PSYCHO](https://zhenxianli.github.io/LISQM/projects/pd-psycho.md): Pure Data, C; established; Pure Data library of psychoacoustic objects (C externals and abstractions) from the author's research on dissonance models.
- [PyDSM](https://zhenxianli.github.io/LISQM/projects/pydsm.md): Python; established; Python toolbox for the design and simulation of delta-sigma modulators.
- [MetaSona](https://zhenxianli.github.io/LISQM/projects/metasona.md): C, Python; newly released (not yet seen to be widely used); C library with a typed Python interface and prebuilt wheels, written to make repeated psychoacoustic analysis fast.
- [ecma418-torch](https://zhenxianli.github.io/LISQM/projects/ecma418-torch.md): Python; newly released (not yet seen to be widely used); Roughness (Clause 7) and fluctuation strength (Clause 9) of ECMA-418-2:2025 (4th edition) in PyTorch, differentiable end to end, with the binaural combination of two channels.
- [iso532-1-rs](https://zhenxianli.github.io/LISQM/projects/iso532-1-rs.md): Rust, C, Python; newly released (not yet seen to be widely used); Rust engine (README title "ISO532") for ISO 532-1:2017 Zwicker loudness: stationary, time-varying and a streaming interface, with a C ABI and a Python binding (PyO3/maturin).
- [Kirin Hypha](https://zhenxianli.github.io/LISQM/projects/kirin-hypha.md): Rust; newly released (not yet seen to be widely used); Free PRE/POST measurement plug-in for mixing and mastering.
- [Mosqito.NET](https://zhenxianli.github.io/LISQM/projects/mosqito-net.md): C#; newly released (not yet seen to be widely used); C# (.NET 8) port of MoSQITo 1.2.1 with the same metrics: ISO 532-1 loudness, ECMA-418-2 (2nd edition) loudness and roughness, DIN 45692 sharpness, Daniel & Weber roughness, and ECMA-74 tone-to-noise and prominence ratios.
- [NumpyLibforPsychoAcoustic](https://zhenxianli.github.io/LISQM/projects/numpylib-psychoacoustic.md): Python; newly released (not yet seen to be widely used); Python (NumPy) adaptation of loudness models from the Auditory Modeling Toolbox (AMT), written as a second-year student project at Mines School of Nancy: the stationary model of Moore, Glasberg & Baer (1997) and the time-varying model of Glasberg & Moore (2002).
- [phonometry](https://zhenxianli.github.io/LISQM/projects/phonometry.md): Python; newly released (not yet seen to be widely used); Python acoustics library (formerly PyOctaveBand) that added a large psychoacoustics module in July 2026: ISO 532-1, -2 and -3 loudness, ECMA-418-2:2025 loudness, tonality, roughness and fluctuation strength (monaural), DIN 45692 sharpness, Osses fluctuation strength, ECMA-418-1:2024 TNR/PR, tonal audibility, impulsiveness, EPNL and ISO 226:2023.
- [PsychoacousticMetrics.jl](https://zhenxianli.github.io/LISQM/projects/psychoacousticmetrics-jl.md): Julia; newly released (not yet seen to be widely used); Julia package for the Zwicker family of sound-quality metrics on top of ZwickerLoudness.jl: DIN 45692:2009 sharpness (with Aures, von Bismarck and Fastl weightings), Daniel & Weber roughness, Osses et al. (2016) fluctuation strength and Widmann (1992) psychoacoustic annoyance.
- [rota-order](https://zhenxianli.github.io/LISQM/projects/rota-order.md): Python; newly released (not yet seen to be widely used); Order analysis of noise and vibration from rotating machinery such as engines, electric motors and gearboxes.
- [SoundPalette](https://zhenxianli.github.io/LISQM/projects/soundpalette.md): C++; newly released (not yet seen to be widely used); C++ tool for game audio that analyses a folder of sound effects and draws each file as a glyph in a palette, built as a static library, a command-line tool (including a lint mode for CI) and a desktop app.
- [torch_amt](https://zhenxianli.github.io/LISQM/projects/torch-amt.md): Python; newly released (not yet seen to be widely used); PyTorch implementations of models from the MATLAB Auditory Modeling Toolbox (AMT), differentiable and able to run on GPU.
- [ZwickerLoudness.jl](https://zhenxianli.github.io/LISQM/projects/zwickerloudness-jl.md): Julia; newly released (not yet seen to be widely used); Julia package that computes ISO 532-1:2017 Zwicker loudness from one-third-octave band levels: the stationary method (clause 5) from 28 band levels and, since v0.3.0, the time-varying method (clause 6) with N5 and N10 from a band-level time series.
- [pySQAT](https://zhenxianli.github.io/LISQM/projects/pysqat.md): Python; developing; Python port of the MATLAB toolbox SQAT (README title "SQAT4PY") with a PySide6 graphical interface.
- [LoudnessModel](https://zhenxianli.github.io/LISQM/projects/loudnessmodel.md): Python; developing; Python translation of the Cambridge MATLAB code for the TVL2018 loudness model: Moore et al. (2016) with the revised time constants of Moore et al. (2018).
- [deeuu/loudness](https://zhenxianli.github.io/LISQM/projects/deeuu-loudness.md): C++, Python; legacy, no commit since 2019-08; C++ library with SWIG Python bindings for building loudness models from cascaded processing modules.
- [fluctuation-strength-TUe](https://zhenxianli.github.io/LISQM/projects/fluctuation-strength-tue.md): MATLAB; legacy, no commit since 2020-01; MATLAB code of the fluctuation strength model of Osses, García & Kohlrausch (2016) (FluctuationStrength_TUe.m), with the stimuli of the paper and a demo that reproduces its figures.
- [HAW loudness tool](https://zhenxianli.github.io/LISQM/projects/haw-specific-loudness.md): MATLAB; legacy, no commit since 2022-12; MATLAB tool with a graphical interface, from HAW Hamburg, that shows the specific loudness of time-varying sounds: a Moore–Glasberg front end on ERB bands with temporal processing after Glasberg & Moore (2002), Chalupper & Fastl (2002) or Baumgarte (2000).
- [IoSR Matlab Toolbox](https://zhenxianli.github.io/LISQM/projects/iosr-matlab-toolbox.md): MATLAB; legacy, no commit since 2017-08; General-purpose MATLAB toolbox from the Institute of Sound Recording (University of Surrey) for auditory modelling, signal processing, source separation, statistics and plotting.
- [MoSQITo-FDP](https://zhenxianli.github.io/LISQM/projects/mosqito-fdp.md): Python; legacy, archived; Archived Python snapshot of a MoSQITo fork made for a final-degree project.
- [PsychoacousticParametersMeasurer](https://zhenxianli.github.io/LISQM/projects/psychoacoustic-parameters-measurer.md): Python; legacy, no commit since 2021-06; Python scripts, commented in Spanish, that translate psychoacoustic parameters described in Zwicker and Fastl's book: Zwicker loudness (stationary and time-varying, from third-octave levels), sharpness, roughness, fluctuation strength and psychoacoustic annoyance.
- [PsySound3](https://zhenxianli.github.io/LISQM/projects/psysound3.md): MATLAB; legacy, no commit since 2015-03; MATLAB program with a graphical interface for analysing sound recordings.
- [python-acoustics](https://zhenxianli.github.io/LISQM/projects/python-acoustics.md): Python; legacy, archived; General Python library for acousticians, published on PyPI as acoustics.
- [SUAVE](https://zhenxianli.github.io/LISQM/projects/suave.md): Python; legacy, no commit since 2022-12; Python environment for the multi-fidelity conceptual design of aircraft ("An Aerospace Vehicle Environment").
- [Swift & Gee codes](https://zhenxianli.github.io/LISQM/projects/rapid-loudness-sharpness.md): MATLAB; legacy, no commit since 2020-01; MATLAB code by S. Hales Swift (Brigham Young University) on MATLAB File Exchange for fast Moore–Glasberg loudness: ANSI S3.4-2007 stationary loudness and Glasberg & Moore (2002) time-varying loudness, each with the sharpness of Swift & Gee (2017), which is computed from Moore–Glasberg specific loudness.
- [Zhen-Ni/epnl](https://zhenxianli.github.io/LISQM/projects/zhen-ni-epnl.md): Rust; legacy, no commit since 2023-07; Small Rust crate that computes perceived noisiness (noy), perceived noise level (PNL) and tone-corrected perceived noise level (PNLT) from 24 one-third-octave band levels, citing ICAO Annex 16, Volume I, Appendix 2 in its code comments.
- [zwickerloudness](https://zhenxianli.github.io/LISQM/projects/zwickerloudness-sourceforge.md): C; legacy, no commit since 2018-06; Console program in C, for BSD, Linux and Windows, that calculates the loudness of stationary sounds with Zwicker's method (ISO 532B / DIN 45631) from one-third-octave band levels.

## Others

Tools that do not compute the metrics themselves: interfaces, front ends and wrappers that call one of the listed projects. They are not listed under the metrics.

- [PsychoBox](https://zhenxianli.github.io/LISQM/projects/psychobox.md): Python; calls MoSQITo; Desktop application (PySide6 GUI) for playing, editing, filtering and analysing audio.
- [Soundscapy](https://zhenxianli.github.io/LISQM/projects/soundscapy.md): Python; calls MoSQITo; Python library for soundscape assessment.
- [ZwickerLoudnessAudio.jl](https://zhenxianli.github.io/LISQM/projects/zwickerloudnessaudio-jl.md): Julia; calls ZwickerLoudness.jl; newly released (not yet seen to be widely used); Julia front end to ZwickerLoudness.jl that computes ISO 532-1:2017 Zwicker loudness from waveforms or WAV files: stationary loudness, and since v0.3.0 time-varying loudness with N5 and N10.

## Status unknown

Projects whose code could not be opened, for example because the repository needs a login. What they implement is unverified, so they are listed with what they claim and not under the metrics.

- [PsyTools](https://zhenxianli.github.io/LISQM/projects/psytools.md): Python; the repository cannot be opened without a login; claims: A conference paper describes an open-source Python toolbox for standardised psychoacoustic analysis: loudness, sharpness, fluctuation strength, roughness, tonality, psychoacoustic annoyance, LAeq and LAmax, following ISO 532-1, DIN 45692 and ECMA-418, with the Zwicker model and the Sottek Hearing Model.

## Data

- [index.json](https://zhenxianli.github.io/LISQM/index.json): the complete index as JSON
- [references.bib](https://zhenxianli.github.io/LISQM/references.bib): every standard, model paper and software paper as BibTeX
- [llms-full.txt](https://zhenxianli.github.io/LISQM/llms-full.txt): all pages in one Markdown file
- [Data schema](https://github.com/ZhenxianLi/LISQM/blob/main/data/SCHEMA.md)

## Optional

- [For AI agents](https://zhenxianli.github.io/LISQM/ai.md): how to retrieve, read and cite this list
- [Metrics](https://zhenxianli.github.io/LISQM/metrics/index.md): every metric with its current edition
- [Languages](https://zhenxianli.github.io/LISQM/languages.md): coverage and calling details by language
- [Project map](https://zhenxianli.github.io/LISQM/projects/map.md): code ported or adapted from another project, use at run time, results checked against another project, and shared contributors
- [Frequently asked questions](https://zhenxianli.github.io/LISQM/faq.md)
- [About](https://zhenxianli.github.io/LISQM/about.md): scope, how entries are checked, definitions and data access
- [Standards timeline](https://zhenxianli.github.io/LISQM/standards.md)
- [Updates](https://zhenxianli.github.io/LISQM/updates.md)
- [Source repository](https://github.com/ZhenxianLi/LISQM)