Less is more: Faster and better music version identification with embedding distillation

Yesiler, Furkan; Serrà, Joan

doi:10.5281/zenodo.4245570

2020

DOI: 10.5281/zenodo.4245570

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Less is more: Faster and better music version identification with embedding distillation

Furkan Yesiler

Joan Serrà

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Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Yesiler

Molina²,

Serrà³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and timestamps for all the tracks played in live music events. Due to various musical and non-musical changes in live performances, developing automatic SLI systems is still a challenging task that, despite its industrial relevance, has been under-explored in the academic literature. In this paper, we propose an end-to-end workflow that identifies relevant metadata and timestamps of live music performances using a version identification system. We compare 3 of such systems to investigate their suitability for this particular task. For developing and evaluating SLI systems, we also contribute a new dataset that contains 99.5 h of concerts with annotated metadata and timestamps, along with the corresponding reference set. The dataset is categorized by audio qualities and genres to analyze the performance of SLI systems in different use cases. Our approach can identify 68% of the annotated segments, with values ranging from 35% to 77% based on the genre. Finally, we evaluate our approach against a database of 56.8 k songs to illustrate the effect of expanding the reference set, where we can still identify 56% of the annotated segments.

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Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Yesiler

Molina²,

Serrà³

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

ByteCover: Cover Song Identification via Multi-Loss Training

Zhu

et al. 2020

Preprint

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Less is more: Faster and better music version identification with embedding distillation

Cited by 2 publications

References 0 publications

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

ByteCover: Cover Song Identification via Multi-Loss Training

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