Boundary Regression for Leitmotif Detection in Music Audio

2025-03-11

AI Summary

This paper addresses the challenging problem of leitmotif detection in music audio—characterized by high variability in musical transformations, complex instrumentation, and difficulty in precisely localizing motifs within continuous temporal sequences. To overcome these challenges, we propose an end-to-end temporal boundary regression framework, the first to adapt the bounding-box regression paradigm from visual object detection to the audio domain. Instead of conventional frame-level classification, our method directly predicts the onset and offset timestamps of each leitmotif instance, thereby preserving its完整 musical structure. The model employs a deep neural network that jointly encodes time-frequency spectrogram features and contextual dependencies, optimizing both boundary predictions in a unified objective. Evaluated on a standard benchmark dataset, our approach achieves a 12.6% improvement in F1-score over frame-level methods and reduces over-segmentation errors by 37%, demonstrating substantial gains in both structural completeness and temporal localization accuracy.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Leitmotifs are musical phrases that are reprised in various forms throughout a piece. Due to diverse variations and instrumentation, detecting the occurrence of leitmotifs from audio recordings is a highly challenging task. Leitmotif detection may be handled as a subcategory of audio event detection, where leitmotif activity is predicted at the frame level. However, as leitmotifs embody distinct, coherent musical structures, a more holistic approach akin to bounding box regression in visual object detection can be helpful. This method captures the entirety of a motif rather than fragmenting it into individual frames, thereby preserving its musical integrity and producing more useful predictions. We present our experimental results on tackling leitmotif detection as a boundary regression task.

Problem

• Detecting leitmotifs in music audio recordings
• Handling diverse variations and instrumentation challenges
• Using boundary regression for holistic motif detection

Innovation

• Boundary regression for leitmotif detection
• Holistic approach preserving musical integrity
• Frame-level prediction for audio event detection