Match filter application in beat tracking by specific instrument sound in drum set music sequence

Hanwen Zhang

doi:10.1117/12.2645525

23 August 2022 Match filter application in beat tracking by specific instrument sound in drum set music sequence

Hanwen Zhang

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050S (2022) https://doi.org/10.1117/12.2645525
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

Instrument sound extraction and beat marking in music signal processing are important applications for analyzing the emotions expressed in music. this paper focuses on the extraction and localization of snare drum and bucket drum sounds in the musical loop sequence of a drum set. Firstly, the author uses artificially extracted snare drum and bucket drum sounds from a drum music loop database to construct two average model to represent the general sound model. Then the pre-processed signals are filtered by matching the snare drum average models of the snare drum with the input drum music loop signal through a cascaded matched filter. after that, the pre-processed signal is sketched by a local maximum sampling operation to useless features. Then the overall signal is binarized to 0 and 1 by an adaptive threshold. Afterwards the point which value is 1, is considered as the onset for potential snare drum sound signal. with comparing the difference in amplitude and the difference in variance distribution for all possible snare drum sound signal, the position of final target signal in input drum music loop signal is confirmed. Finally, I suppress snare drum signal derived from the first layer in the original sound and repeat the above steps for the detection and tracking of the barrel drum. To demonstrate the robustness of the method, I use soft-sound sources databases and expanded the number of samples to 210 by random slicing, and by comparing the original data, I found that the method achieved an overall segmentation accuracy of 80.5% and a correct detection rate of 94.4%% in a cyclic sequence of drum kit sounds without consecutive hits. This reflects that the algorithm also gives acceptable results compared to other methods.

Citation Download Citation

Hanwen Zhang "Match filter application in beat tracking by specific instrument sound in drum set music sequence", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050S (23 August 2022); https://doi.org/10.1117/12.2645525

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KEYWORDS

Signal detection

Electronic filtering

Signal processing

Databases

Detection and tracking algorithms

Filtering (signal processing)

Target detection

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