Machine learning / deep learning approach to soundscape evaluations
Soundscape augmentation is a paradigm shift in noise mitigation by placing greater emphasis on the human perception of the acoustic environment. This is performed by introducing additional sounds to mask the background noise for better acoustic comfort. Evaluation of such data is crucial for the...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177228 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Soundscape augmentation is a paradigm shift in noise mitigation by placing greater emphasis
on the human perception of the acoustic environment. This is performed by introducing
additional sounds to mask the background noise for better acoustic comfort. Evaluation of
such data is crucial for the understanding of the diversity of responses for different
demographics.
This study aims to improve the IoT subsystem for adaptive soundscape augmentation, which
is done in another study called ‘Deployment of an IoT System for Adaptive In-Situ
Soundscape Augmentation’ [1], by classifying demographic-sensitive perceptions of sound
through the incorporation of live age and gender detection. Deep learning models, based on
MiVOLO [2], are employed to accommodate the variations in height, angles, and distances.
The robustness of the model is then validated through a series of controlled experiments,
employing a diverse dataset to ensure inclusivity and accuracy. |
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