Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Yesiler, Furkan; Molina, Emilio; Serrà, Joan

doi:10.1109/icassp39728.2021.9414603

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the related task of cover song detection has a long history of study in music information retrieval (Serrà et al, 2010;Yesiler et al, 2019), to our knowledge no audio similarity algorithms had been tested for their ability to evaluate copyright infringement until our preliminary study (Yuan et al, 2020). However, many general audio similarity algorithms have been evaluated through the Music Information Retrieval Evaluation Exchange (MIREX) competition.…”

Section: Automatic Analysismentioning

confidence: 99%

Perceptual and automated estimates of infringement in 40 music copyright cases

Yuan,

Cronin,

Müllensiefen

et al. 2023

TISMIR

View full text Add to dashboard Cite

Music copyright infringement lawsuits implicate millions of dollars in damages and costs of litigation. There are, however, few objective measures by which to evaluate these claims. Recent music information retrieval research has proposed objective algorithms to automatically detect musical similarity, which might reduce subjectivity in music copyright infringement decisions, but there remains minimal relevant perceptual data despite its crucial role in copyright law. We collected perceptual data from 51 participants for 40 adjudicated copyright cases from 1915–2018 in 7 legal jurisdictions (USA, UK, Australia, New Zealand, Japan, People’s Republic of China, and Taiwan). Each case was represented by three different versions: either full audio, melody only (MIDI), or lyrics only (text). Due to the historical emphasis in legal opinions on melody as the key criterion for deciding infringement, we originally predicted that listening to melody-only versions would result in perceptual judgments that more closely matched actual past legal decisions. However, as in our preliminary study of 17 court decisions (Yuan et al., 2020), our results did not match these predictions. Participants listening to full audio outperformed not only the melody-only condition, but also automated algorithms designed to calculate musical similarity (with maximal accuracy of 83% vs. 75%, respectively). Meanwhile, lyrics-only conditions performed at chance levels. Analysis of outlier cases suggests that music, lyrics, and contextual factors can interact in complex ways difficult to capture using quantitative metrics. We propose directions for further investigation including using larger and more diverse samples of cases, enhanced methods, and adapting our perceptual experiment method to avoid relying on ground truth data only from court decisions (which may be subject to errors and selection bias). Our results contribute data and methods to inform practical debates relevant to music copyright law throughout the world, such as the question of whether, and the extent to which, judges and jurors should be allowed to hear published sound recordings of the disputed works in determining musical similarity. Our results ultimately suggest that while automated algorithms are unlikely to replace human judgments, they may help to supplement them.

show abstract

Section: Automatic Analysismentioning

confidence: 99%

Perceptual and automated estimates of infringement in 40 music copyright cases

Yuan,

Cronin,

Müllensiefen

et al. 2023

TISMIR

View full text Add to dashboard Cite

show abstract

“…The lead sheet is strongly linked to the f0 contours and chroma features of music, both of which play significant roles in cover song identification[23,24,34].…”

mentioning

confidence: 99%

PiCoGen: Generate Piano Covers with a Two-stage Approach

Tan,

Guan,

Yang

2024

Proceedings of the 2024 International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

Cover song generation stands out as a popular way of music making in the music-creative community. In this study, we introduce Piano Cover Generation (PiCoGen), a two-stage approach for automatic cover song generation that transcribes the melody line and chord progression of a song given its audio recording, and then uses the resulting lead sheet as the condition to generate a piano cover in the symbolic domain. This approach is advantageous in that it does not required paired data of covers and their original songs for training. Compared to an existing approach that demands such paired data, our evaluation shows that PiCoGen demonstrates competitive or even superior performance across songs of different musical genres. CCS CONCEPTS• Applied computing → Sound and music computing; • Information systems → Multimedia content creation.

show abstract

Investigating the Efficacy of Music Version Retrieval Systems for Setlist Identification

Cited by 2 publications

References 11 publications

Perceptual and automated estimates of infringement in 40 music copyright cases

Perceptual and automated estimates of infringement in 40 music copyright cases

PiCoGen: Generate Piano Covers with a Two-stage Approach

Contact Info

Product

Resources

About