| Autor: |
M. A., Rohit, Bhattacharjee, Amitrajit, Rao, Preeti |
| Předmět: |
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| Zdroj: |
International Society for Music Information Retrieval Conference Proceedings; 2021, p19-26, 8p |
| Abstrakt: |
Motivated by musicological applications of the four-way categorization of tabla strokes, we consider automatic classification methods that are potentially robust to instrument differences. We present a new, diverse tabla dataset suitably annotated for the task. The acoustic correspondence between the tabla stroke categories and the common popular Western drum types motivates us to adapt models and methods from automatic drum transcription. We start by exploring the use of transfer learning on a state-of-the-art pre-trained multiclass CNN drums model. This is compared with 1-way models trained separately for each tabla stroke class. We find that the 1-way models provide the best mean f-score while the drums pre-trained and tablaadapted 3-way models generalize better for the most scarce target class. To improve model robustness further, we investigate both drums and tabla-specific data augmentation strategies. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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