Skip to main content

Modular Sound Quality Integrated Toolbox

Project description



Sound quality (SQ) metrics are developed by acoustic engineers and researchers to provide an objective assessment of the pleasantness of a sound. Different metrics exist depending on the nature of the sound to be tested. Some of these metrics are already standardized, while some others rely on scientific articles and are still under active development. The calculation of some sound quality metrics are included in major commercial acoustic and vibration measurement and analysis software. However, some of the proposed metrics results from in-house implementation and can be dependent from one system to another. Some implementations may also lack of complete documentation and validation on publicly available standardized sound samples. Several implementations of SQ metrics in different languages can been found online, confirming the interest of the engineering and scientific community, but they often use Matlab signal processing commercial toolbox.


The objective of MoSQITo is therefore to provide a unified and modular development framework of key sound quality metrics with open-source object-oriented technologies, favoring reproducible science and efficient shared scripting among engineers, teachers and researchers community.

It is written in Python, one of the most popular free programming language in the scientific computing community. It is meant to be highly documented (use of Jupyter notebooks, tutorials) and validated with reference sound samples and scientific publications.

Origin of the project

EOMYS initiated this open-source project in 2020 as a side-project to Pyleecan (Python Library for Electrical Engineering Computational Analysis) an open and non-commercial project started two years earlier.


Tutorials are available in the tutorials folder. Documentation and validation of the MoSQITo functions are available in the documentation folder.


The scope of the project is to implement the following first set of metrics:

Reference Available Under dev. To do
Loudness for
steady signals
(Zwicker method)
ISO 532B:1975
DIN 45631:1991
ISO 532-1:2017 §5
Loudness for non-stationary
(Zwicker method)
DIN 45631/A1:2010
ISO 532-1:2017 §6
Sharpness DIN 45692:2009 x
Roughness /
Fluctuation Strength
To be defined x
Tonality (Hearing model) ECMA-74:2019 annex G x

As a second priority, the project could address the following metrics:

Reference Available Under dev. To do
Loudness for steady signals
(Moore/Glasberg method)
ISO 532-2:2017 x
Loudness for non-stationary
(Moore/Glasberg method)
Moore, 2014 x
Sharpness (using
Moore/Glasberg loudness)
and Gee, 2017
Tone-to-noise ratio / Prominence
ratio (occupational noise,
discrete tones)
ECMA-74:2019 annex D
ISO 7719:2018
Tone-to-noise ratio
(environmental noise,
automatic tone detection)
DIN 45681 x
Tone-to-noise ratio
(environmental noise)
ISO 1996-2 x
Tone-to-noise ratio
(environmental noise)
ANSI S1.13:2005 x

In parallel, tools for signal listening and manipulation will be developed. The objective is to be able to apply some modification to a signal (filtering, tone removal, etc.) and assess the impact on different SQ metrics.

Of course, any other sound quality related implementation by anyone who wants to contribute is welcome.


You can contact us on Github by opening an issue (to request a feature, ask a question or report a bug).


Hales Swift, S., and Gee, K. L. (2017). “Extending sharpness calculation for an alternative loudness metric input,” J. Acoust. Soc. Am.142, EL549.

Moore, B. C. J. (2014). “Development and Current Status of the “Cambridge” Loudness Models,” Trends in Hearing, vol. 18: 1-29

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mosqito-0.1.0.tar.gz (34.4 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page