Modular Sound Quality Integrated Toolbox
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.
Besides the metrics, sound quality studies requires several tool like audio signal filtering or jury testing procedure for instance.
The objective of MOSQITO is therefore to provide a unified and modular development framework of key sound quality tools (including key SQ metrics) with open-source object-oriented technologies, favoring reproducible science and efficient shared scripting among engineers, teachers and researchers community. The development roadmap of the project is presented in more details in the scope section of the documentation.
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 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.
MOSQITO is available on pip. Simply type in a shell the following command:
pip install mosqito
This command line should download and install MOSQITO on your computer, along with the dependencies needed to compute SQ metrics.
If you need to import .uff or .unv files, you will need the pyuff package dependency. Note that 'pyuff' is released under the GPL license which prevents MOSQITO from being used in other software that must be under a more permissive license. To include the 'pyuff' dependancy anyway, type the following command:
pip install mosqito[uff]
If you want to use MOSQITO coupled with SciDataTool, you will need SDT package dependency. To install it along with MOSQITO, use the following command:
pip install mosqito[SciDataTool]
Note that all the depencies can be installed at once using:
pip install mosqito[all]
You can contact us on Github by opening an issue (to request a feature, ask a question or report a bug).
If you are using MOSQITO in your research activities, please help our scientific visibility by citing our work! You can use the following citation in APA format:
Green Forge Coop. MOSQITO [Computer software]. https://doi.org/10.5281/zenodo.5284054
If you need to cite the current release of MOSQITO, please use the "Cite this repository" feature in the "About" section of this Github repository.
Publications citing MOSQITO
M. Glesser, S. Wanty, K. Degrendele, J. L. Besnerais, and S. Ni, “Perceived sound quality analysis of Electric Drive Units under different switching control strategies,” in 12th Aachen Acoustic Colloquium, Aachen, Nov. 2021.
M. Glesser, S. Ni, K. Degrendele, S. Wanty, and J. L. Besnerais, “Sound quality analysis of Electric Drive Units under differentswitching control strategies,” in SIA Automotive NVH comfort, Le Mans, 2021.
San Millán-Castillo, R., Latorre-Iglesias, E., Glesser, M., Wanty, S., Jiménez-Caminero, D., & Álvarez-Jimeno, J.M. (2021). MOSQITO: an open-source and free toolbox for sound quality metrics in the industry and education. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 12, 1164-1175. https://doi.org/10.3397/IN-2021-1767
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.