Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911948639043584 |
|---|---|
| author | Miyaguchi, Anthony Zhong, Nathan Gustineli, Murilo Hayduk, Chris |
| author_facet | Miyaguchi, Anthony Zhong, Nathan Gustineli, Murilo Hayduk, Chris |
| contents | We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing off-the-shelf models, BirdNET and MixIT, to address representation and labeling challenges in the competition. We explore the embedding space learned by BirdNET and propose a process to derive an annotated dataset for supervised learning. Our experiments involve various models and feature engineering approaches to maximize performance on the competition leaderboard. The results demonstrate the effectiveness of our approach in classifying bird species and highlight the potential of transfer learning and semi-supervised dataset annotation in similar tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16760 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification Miyaguchi, Anthony Zhong, Nathan Gustineli, Murilo Hayduk, Chris Sound Information Retrieval Machine Learning Audio and Speech Processing We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing off-the-shelf models, BirdNET and MixIT, to address representation and labeling challenges in the competition. We explore the embedding space learned by BirdNET and propose a process to derive an annotated dataset for supervised learning. Our experiments involve various models and feature engineering approaches to maximize performance on the competition leaderboard. The results demonstrate the effectiveness of our approach in classifying bird species and highlight the potential of transfer learning and semi-supervised dataset annotation in similar tasks. |
| title | Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification |
| topic | Sound Information Retrieval Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2306.16760 |