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Communication Dans Un Congrès Année : 2024

Microphone-based Data Augmentation for Automatic Recognition of Instrumental Playing Techniques

Résumé

Within existing research on the automatic classification of musical instrument playing techniques, few available datasets include enough playing techniques to cover the full range of a given musical instrument’s expressive ability. However, creating a new large dataset requires recording many samples for many performance techniques, which is costly and time-consuming. Therefore, in this study, we attempt to augment data by increasing the number of recording microphones without increasing the recording duration and verify the effectiveness of this data augmentation method. As a result of recording flute playing techniques using multiple microphones, the accuracy and macro F1-Score of a convolutional neural network-based classifier improved when using a combination of the five most close-to-source microphones. The classifier’s performance further improved when data were combined with a data augmentation method based on pitch shifting.
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Dates et versions

hal-04642673 , version 1 (10-07-2024)

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  • HAL Id : hal-04642673 , version 1

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Nicolas Brochec, Tsubasa Tanaka, Will Howie. Microphone-based Data Augmentation for Automatic Recognition of Instrumental Playing Techniques. International Computer Music Conference (ICMC 2024), Jul 2024, Seoul, South Korea. ⟨hal-04642673⟩
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