Date of Award
Spring 6-13-2025
Document Type
Masters Thesis
Degree Name
M.S. in Computer Science
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
First Advisor
Stephen Hutt
Second Advisor
Andrew Hannum
Third Advisor
Margareta Stefanovic
Copyright Statement / License for Reuse
All Rights Reserved.
Keywords
Artificial intelligence (AI), Audio signal processing, Drum classification, Machine learning, Multi-label classification, Music
Abstract
This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications in music production, sound design, and automated drum transcription.
Copyright Date
6-2025
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Sean Perman
Provenance
Received from ProQuest
File Format
application/pdf
Language
English (eng)
Extent
56 pgs
File Size
5.3 MB
Recommended Citation
Perman, Sean, "Multi-Label Classification of Acoustic and Electronic Drum Sounds Using Machine Learning" (2025). Electronic Theses and Dissertations. 2589.
https://digitalcommons.du.edu/etd/2589
Included in
Artificial Intelligence and Robotics Commons, Other Music Commons, Theory and Algorithms Commons