MOSQITO sound quality library#
Version: 1.1.1
The objective of MOSQITO is to provide a unified and modular development framework of key sound quality tools (including key SQ metrics), 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 of the project.
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 and validated with reference sound samples and scientific publications.
Background#
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.
Contact#
You can contact us on Github by opening an issue (to request a feature, ask a question or report a bug). Eomys/MoSQITo
How to cite MOSQITO#
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 (Version 1.1.1). https://doi.org/10.5281/zenodo.10629475
If you need to cite the current release of MOSQITO, please use the “Cite this repository” feature in the “About” section of the Github repository.
Softwares using MOSQITO#
Manatee: A software solution for the assessment and control of magnetic noise & vibrations in electric drives
Soundscapy: A python library for analysing and visualising soundscape assessments.
miniDSP: An acquisition tool with psychoacoustic parameters estimation.
Publications citing MOSQITO#
E. Gallo, G. Beaulieu, and C.F. Schram. Annoyance factors of a maneuvering multicopter drone. 28th AIAA Aviation and aeroacoustics conference, 2022. URL: https://doi.org/10.2514/6.2022-2837.
M. Glesser, S. Ni, K. Degrendele, S. Wanty, and J. Le Besnerais. Sound quality analysis of electric drive units under different switching control strategies. SIA Automotive NVH comfort, 2021. URL: https://www.researchgate.net/publication/356217087_Sound_quality_analysis_of_Electric_Drive_Units_under_different_switching_control_strategies.
M. Glesser, S. Wanty, K. Degrendele, J. Le Besnerais, and S. Ni. Perceived sound quality analysis of electric drive units under different switching control strategies. 12th Aachen Acoustic Colloquium, 2021. URL: https://e-nvh.eomys.com/aachen-acoustics-colloquium-21-perceived-sound-quality-analysis-of-electric-drive-units-under-different-switching-control-strategies/.
W. F. Menegatt. Desempenho de métodos de avaliação subjetiva online para quantificar a irritabilidade do ruído de refrigeradores. Master's thesis, Universidade Federal de Santa Catarina, Programa de Pós-Graduação, Florianópolis, 2022. URL: https://repositorio.ufsc.br/handle/123456789/241023.
R. San Millán-Castillo, E. Latorre-Iglesias, and M. Glesser. Engagement capstone projects: a collaborative approach to a case study in psychoacoustics. The Journal of the Acoustical Society of America, 2022. URL: https://doi.org/10.1121/10.0014693.
R. San Millán-Castillo, E. Latorre-Iglesias, M. Glesser, S. Wanty, D. Jiménez-Caminero, and J.M. Álvarez-Jimeno. 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, 2021. URL: https://doi.org/10.3397/IN-2021-1767.
Nabila Ajeng Nazla, Sarwono Sugeng Joko, Sudarsono Anugrah Sabdono, Nitidara Ni Putu Amanda, and Zakri Keysha Wellviestu. Evaluation of psychoacoustic parameters on internal combustion engine vehicle and electric vehicle: case study of armored vehicle. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 2023. URL: https://www.ingentaconnect.com/content/ince/incecp/2023/00000268/00000005/art00092#trendmd-suggestions.
K. Ooi, Z.T. Ong, K. Watcharasupat, B. Lam, J.Y. Hong, and W.S. Gan. A large-scale dataset and baseline models of affective responses to augmented urban soundscapes. IEEE Transactions on affective computing, 2022. URL: https://arxiv.org/abs/2207.01078.
E. Ruaud, I. Legriffon, and J. Caillet. Investigation of helicopter noise annoyance and noticeability in urban environment. Forum Acousticum, 2023. URL: https://hal.science/hal-04265818.
P. Wißbrock, Y. Richter, D. Pelkmann, Z. Ren, and G. Palmer. Cutting through the noise: an empirical comparison of psychoacoustic and envelope-based features for machinery fault detection. International conference on acoustics, speech, and signal processing, 2023. URL: https://arxiv.org/abs/2211.01704.