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Unlike melody extraction and other aspects of music transcription, researchon playing technique detection is still in its early stages. Compared toexisting work mostly focused on playing technique detection for individualsingle notes, we propose a general end-to-end method based on Sound EventDetection by FCN for musical instrument playing technique detection. In ourcase, we choose Erhu, a well-known Chinese bowed-stringed instrument, toexperiment with our method. Because of the limitation of FCN, we present analgorithm to detect on variable length audio. The effectiveness of the proposedframework is tested on a new dataset, its categorization of techniques issimilar to our training dataset. The highest accuracy of our 3 experiments onthe new test set is 87.31%. Furthermore, we also evaluate the performance ofthe proposed framework on 10 real-world studio music (produced by midi) and 7real-world recording samples to address the ability of generalization on ourmodel.

Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example