Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example
This work addresses the specific problem of playing technique detection for musicians and researchers in music information retrieval, but it is incremental as it builds on existing FCN methods for a new application.
The authors tackled the problem of detecting musical instrument playing techniques, which is an under-explored area compared to melody extraction, by proposing an end-to-end method based on Sound Event Detection using FCN, specifically applied to the Chinese bowed-stringed instrument Erhu, achieving a highest accuracy of 87.31% on a new test dataset.
Unlike melody extraction and other aspects of music transcription, research on playing technique detection is still in its early stages. Compared to existing work mostly focused on playing technique detection for individual single notes, we propose a general end-to-end method based on Sound Event Detection by FCN for musical instrument playing technique detection. In our case, we choose Erhu, a well-known Chinese bowed-stringed instrument, to experiment with our method. Because of the limitation of FCN, we present an algorithm to detect on variable length audio. The effectiveness of the proposed framework is tested on a new dataset, its categorization of techniques is similar to our training dataset. The highest accuracy of our 3 experiments on the new test set is 87.31%. Furthermore, we also evaluate the performance of the proposed framework on 10 real-world studio music (produced by midi) and 7 real-world recording samples to address the ability of generalization on our model.