Automatic Live Music Song Identification Using Multi-level Deep Sequence Similarity Learning

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ID: 283527
2025
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Abstract
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.
Reference Key
virtanen2025automatic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aapo Hakala; Trevor Kincy; Tuomas Virtanen
Journal arXiv
Year 2025
DOI
DOI not found
URL
http://arxiv.org/abs/2501.08129v1
Keywords
eess.as cs.sd

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