Paper ID: 2501.08129 • Published Jan 14, 2025
Automatic Live Music Song Identification Using Multi-level Deep Sequence Similarity Learning
Aapo Hakala, Trevor Kincy, Tuomas Virtanen
TL;DR
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This paper studies the novel problem of automatic live music song
identification, where the goal is, given a live recording of a song, to
retrieve the corresponding studio version of the song from a music database. We
propose a system based on similarity learning and a Siamese convolutional
neural network-based model. The model uses cross-similarity matrices of
multi-level deep sequences to measure musical similarity between different
audio tracks. A manually collected custom live music dataset is used to test
the performance of the system with live music. The results of the experiments
show that the system is able to identify 87.4% of the given live music queries.