2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-20.2
Paper Title INVESTIGATING THE EFFICACY OF MUSIC VERSION RETRIEVAL SYSTEMS FOR SETLIST IDENTIFICATION
Authors Furkan Yesiler, Music Technology Group, Universitat Pompeu Fabra, Spain; Emilio Molina, BMAT Licensing S.L., Spain; Joan Serrà, Dolby Laboratories, Spain; Emilia Gómez, Joint Research Centre, European Commission, Spain
SessionAUD-20: Music Information Retrieval and Music Language Processing 3: Topics in Music Information Retrieval
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-MIR] Music Information Retrieval and Music Language Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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 99.5h 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 56.8k songs to illustrate the effect of expanding the reference set, where we can still identify 56% of the annotated segments.