Real-World Environment Simulation for Validation of AI Sound Detection and Localization

Banchero L, Lopez JJ. Real-World Environment Simulation for Validation of AI Sound Detection and Localization. In: Express Paper. Audio Engineering Society; 2024. Paper 198. Available from: https://aes.org/publications/elibrary-page/?id=22544

@inproceedings{Banchero2024_22544,
  author = {Banchero, Lucas and Lopez, Jose Javier},
  title = {{Real-World Environment Simulation for Validation of AI Sound Detection and Localization}},
  note = {Paper 198},
  year = {2024},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22544}
}

TY  - CPAPER
TI  - Real-World Environment Simulation for Validation of AI Sound Detection and Localization
AU  - Banchero, Lucas
AU  - Lopez, Jose Javier
M1  - Paper 198
PY  - 2024
DA  - 2024/06/06
UR  - https://aes.org/publications/elibrary-page/?id=22544
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents an experimental framework designed to evaluate the performance of Deep Neural Networks (DNNs) in detecting and localizing audio signals in a controlled laboratory setting. Departing from conventional validation methods, our methodology emphasizes the importance of a precisely configured laboratory setup to ensure accurate and reliable assessment of DNN capabilities. Central to our approach is the use of Wave-Field Synthesis (WFS) technology, which enables the recreation of realistic acoustic environments in the laboratory. By leveraging this technology, we can simulate a wide range of acoustic scenarios, allowing for comprehensive testing of DNN performance under varying conditions. Additionally, our methodology incorporates diverse datasets carefully selected to represent real-world audio stimuli. Furthermore, we propose as an example, the development of an AI-based sound detection and localization system tailored for emergency sounds in vehicular environments. This initiative aims to assess the performance of AI systems trained on data from the meticulously constructed validation environment outlined in this study.
ER  -