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.
EOMYS ENGINEERING initiated this open-source project in 2020 for the study of electric motor sound quality. The project is now backed by Green Forge Coop non profit organization, who also supports the development of Pyleecan electrical machine simulation software.
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 | Validated | Available | Under dev. | To do | |
|---|---|---|---|---|---|
| Loudness for steady signals (Zwicker method) |
ISO 532B:1975 DIN 45631:1991 ISO 532-1:2017 §5 |
x | x | ||
| Loudness for non-stationary (Zwicker method) |
DIN 45631/A1:2010 ISO 532-1:2017 §6 |
x | x | ||
| Roughness | Daniel and Weber, 1997 | x | x | ||
| Fluctuation Strength | To be defined | x | |||
| Sharpness | DIN 45692:2009 | x | x | ||
| Tonality (Hearing model) | ECMA-74:2019 annex G | x |
As a second priority, the project could address the following metrics:
| Reference | Validated | 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) |
Hales-Swift and Gee, 2017 |
x | |||
| Tone-to-noise ratio / Prominence ratio (occupational noise, discrete tones) |
ECMA-74:2019 annex D ISO 7719:2018 |
x | |||
| 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).
Daniel, P., and Weber, R. (1997). “Psychoacoustical Roughness: Implementation of an Optimized Model”, Acta Acustica, Vol. 83: 113-123
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