On Feature Learning for Titi Monkey Activity Detection
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909236558036992 |
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| author | Ravuri, Aditya Muir, Jen Lawrence, Neil D. |
| author_facet | Ravuri, Aditya Muir, Jen Lawrence, Neil D. |
| contents | This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classifier, we effectively address the challenges posed by the small amount of expert-annotated vocal data available. Our approach significantly reduces false positives and improves the accuracy of call detection in bioacoustic research. Initial results demonstrate an accuracy of 95\% on instance predictions, highlighting the effectiveness of our model in identifying and classifying complex vocal patterns in environmental audio recordings. Moreover, we show how call classification can be done downstream, paving the way for real-world monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01452 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Feature Learning for Titi Monkey Activity Detection Ravuri, Aditya Muir, Jen Lawrence, Neil D. Sound Audio and Speech Processing This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizing a combination of MFCC features and a bidirectional LSTM-based classifier, we effectively address the challenges posed by the small amount of expert-annotated vocal data available. Our approach significantly reduces false positives and improves the accuracy of call detection in bioacoustic research. Initial results demonstrate an accuracy of 95\% on instance predictions, highlighting the effectiveness of our model in identifying and classifying complex vocal patterns in environmental audio recordings. Moreover, we show how call classification can be done downstream, paving the way for real-world monitoring. |
| title | On Feature Learning for Titi Monkey Activity Detection |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.01452 |