Savage, Cronin, Müllensiefen and Atkinson (2018)

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1. Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare 2. Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)? 3. Harmony of interest assumption between authors and publishers (creators and producers/investors) 4. Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption) 5. Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)

A. Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right) B. Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction) C. Mass digitisation/orphan works (non-use; extended collective licensing) D. Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability) E. Fair remuneration (levies; copyright contracts) F. Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)

Source Details

Savage, Cronin, Müllensiefen and Atkinson (2018)
Title: Quantitative Evaluation of Music Copyright Infringement
Author(s): Savage,P.E., Cronin, C., Müllensiefen, D., Atkinson, Q.D.
Year: 2018
Citation: Savage, P.E., Cronin, C., Müllensiefen, D. And Atkinson, Q.D. (2018) Quantitative Evaluation of Music Copyright Infringement. Conference: Proceedings of the Folk Music Analysis 2018 Workshop.
Link(s): Open Access
Key Related Studies:
Discipline:
Linked by: Yuan et al. (2020)
About the Data
Data Description: The study uses an adapted “percent melodic identity” (PMI) method (more typically used in molecular genetics to compare DNA and protein sequences) to identify shared pitch classes between songs. This automated method was tested on 20 melodic sequences which were the subject of copyright litigation (e.g. comparing The Chiffon’s ‘He’s So Fine’ and George Harrison’s ‘My Sweet Lord’).
Data Type: Primary data
Secondary Data Sources:
Data Collection Methods:
Data Analysis Methods:
Industry(ies):
Country(ies):
Cross Country Study?: No
Comparative Study?: No
Literature review?: No
Government or policy study?: No
Time Period(s) of Collection:
Funder(s):
  • Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT)
  • Rutherford Discovery Fellowship

Abstract

“Unfounded music copyright lawsuits inhibit musical creativity and waste millions of taxpayer dollars annually. Our aim was to develop and test simple quantitative methods in order to supplement traditional qualitative musicological analyses and improve the efficiency and transparency of music copyright lawsuits. We adapted automatic sequence alignment algorithms from computational biology to create a "percent melodic identity" (PMI) method that was initially developed to measure the cultural evolution of folk music from different cultures. This method automatically quantifies and visualizes the percentage of identical pitch classes shared between two melodic sequences. We applied the PMI method to a corpus of 20 pairs of melodies that had been the subject of legal decisions and that had previously been analyzed using automatic methods. We found that the PMI method was able to accurately predict 80% (16/20) of previous decisions, with PMIs below 50% usually resulting in decisions of no infringement (11/13 cases), and PMIs above 50% usually resulting in decisions of infringement (5/7 cases). Importantly, each of the four outlying cases could be explained by contextual factors not related to melodic similarity (e.g., lyrics, access). Our results provide promise for improving music copyright evaluation by supplementing traditional qualitative components with quantitative methods and visualization tools that are simple enough to be useful to juries, judges, and other non-musicologists.”

Main Results of the Study

Where an automated procedure found substantial melodic similarity between pairs of litigated songs, this accurately predicted 80% of court outcomes. Of the four cases which were not accurately predicted, this was largely ascribed to extra-musical factors such as substantially similar lyrics (Grand Upright vs Warner), no proof of access to the work (Selle vs Gibb), or because of the identity of the composer (Fantasy vs Fogerty).These extra-musical factors potentially play a greater role than pure melodic similarity, which the authors note may be a factor in controversial cases such as Marvin Gaye’s ‘Got To Give It Up’ vs Robin Thicke’s ‘Blurred Lines’. The automated PMI method found substantial similarity only in the ‘signature phrase’ of these songs, but when comparing the entirety of both found no similarity.

Policy Implications as Stated By Author

The study confirms that the PMI method of predicting case outcomes may be a viable method of improving efficiency and transparency in cases of alleged music copyright infringement, particularly as the number of litigated cases increases. The authors caution that such algorithms should not replace trial by jury, and in fact changing norms may place greater weight on extra-musical factors as opposed to pure melodic similarities.

Coverage of Study

Coverage of Fundamental Issues
Issue Included within Study
Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare
Green-tick.png
Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)?
Green-tick.png
Harmony of interest assumption between authors and publishers (creators and producers/investors)
Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption)
Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)
Coverage of Evidence Based Policies
Issue Included within Study
Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right)
Green-tick.png
Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction)
Mass digitisation/orphan works (non-use; extended collective licensing)
Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability)
Fair remuneration (levies; copyright contracts)
Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)
Green-tick.png

Datasets

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