GraFPrint: A GNN-Based Approach for Audio Identification

• URL: http://arxiv.org/abs/2410.10994v1
• Date: Mon, 14 Oct 2024 18:20:09 GMT
• Title: GraFPrint: A GNN-Based Approach for Audio Identification
• Authors: Aditya Bhattacharjee, Shubhr Singh, Emmanouil Benetos,
• Abstract summary: GraFPrint is an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio fingerprints. GraFPrint demonstrates superior performance on large-scale datasets at various levels of granularity, proving to be both lightweight and scalable.
• Score: 11.71702857714935
• License:
• Abstract: This paper introduces GraFPrint, an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio fingerprints. Our method constructs a k-nearest neighbor (k-NN) graph from time-frequency representations and applies max-relative graph convolutions to encode local and global information. The network is trained using a self-supervised contrastive approach, which enhances resilience to ambient distortions by optimizing feature representation. GraFPrint demonstrates superior performance on large-scale datasets at various levels of granularity, proving to be both lightweight and scalable, making it suitable for real-world applications with extensive reference databases.

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