Understanding Audio Source Separation in Carnatic Music with Multimodal Data
Year: 2025
Type: dissertation
Abstract: This thesis investigates the internal mechanisms of audio-visual source separation models, focusing on how performer motion guides source separation in Carnatic music.To move beyond "black box" performance metrics, we employ a dual approach combining model-independent analysis of audio-visual synchrony with model-specificinterpretability techniques applied to Voice-Vision Transformer (VoViT) architectures. A cross-correlation and sliding-window analysis on the Saraga dataset first establishesinstrument-specific temporal patterns, revealing that targeted instrumentspecific motion features exhibit stronger and more consistent correlations with audio dynamics than general body motion. While global linear audio-visual synchrony is weak, these analyses highlight the importance of localised and instrument-specific motion cues. Subsequently, we apply gradient-based saliency methods to a vocal separation model, demonstrating its primary reliance on facial keypoints. Ablation studies causally confirm that these facial regions are crucial for vocal separation, while body motion contributes minimally. We further analyze the attention mechanisms of a vocal & violin model to understand how it disentangles spectrally overlapping sources, specifically through a revised hybrid fusion architecture. FiLM (Feature-wise Linear Modulation) analysis reveals that visual information causally modulates audio features by consistently amplifying or suppressing them, acting as a dynamic gating mechanism. This research provides a framework for interpreting gesture-based audio-visual systems, offers novel insights into audio-visual learning, and contributes to the development of more transparent and culturally-aware Music Information Retrieval technologies. Code available at: https://github.com/theofuhrmann/masters-thesis
Author: Fuhrmann, Théo
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7086861301
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