Date of Award
Honors Thesis (Open Access)
Colby College. Computer Science Dept.
Oliver W. Layton
This study examined the task of drum signal separation from full music mixes via both classical methods (Independent Component Analysis) and a combination of Time-Frequency Binary Masking and Convolutional Neural Networks. The results indicate that classical methods relying on predefined computations do not achieve any meaningful results, while convolutional neural networks can achieve imperfect but musically useful results. Furthermore, neural network performance can be improved by data augmentation via transposition – a technique that can only be applied in the context of drum signal separation.
neural networks, sound processing, source separation, machine learning
Recommended CitationOrehovschi, Marius, "Convolutional Audio Source Separation Applied to Drum Signal Separation" (2021). Honors Theses. Paper 1309.