Summary of Audio Simulation For Sound Source Localization in Virtual Evironment, by Yi Di Yuan et al.
Audio Simulation for Sound Source Localization in Virtual Evironment
by Yi Di Yuan, Swee Liang Wong, Jonathan Pan
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the complex problem of non-line-of-sight localization, focusing on indoor scenarios where acoustic methods struggle due to reverberation. The authors employ physically grounded sound propagation simulations and machine learning techniques to pinpoint sound sources within a virtual environment. By leveraging these approaches, they aim to overcome data insufficiency issues in post-event localization tasks. The proposed method achieves an impressive F1-score of 0.786 +/- 0.0136 using an audio transformer spectrogram approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to figure out a way to locate sounds indoors, where it’s hard to do because of the way sound bounces around. They’re using special computer simulations and machine learning techniques to create a virtual environment that can help them find the source of a sound even if there isn’t much data available after an event. |
Keywords
* Artificial intelligence * F1 score * Machine learning * Transformer
