Newman et al. (2020)

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Film and motion pictures Sound recording and music publishing Photographic activities PR and communication Software publishing (including video games) Specialised design Television programmes Translation and interpretation

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

Newman et al. (2020)
Title: Cover Song Identification - A Novel Stem-Based Approach to Improve Song-To-Song Similarity Measurements
Author(s): Newman, L., Shah, D., Vaughn, C., Javed, F.
Year: 2020
Citation: Newman, L., Shah, D., Vaughn, C. and Javed, F. (2020) Cover Song Identification - A Novel Stem-Based Approach to Improve Song-To-Song Similarity Measurements. SMU Data Science Review, 3(2)
Link(s): Open Access
Key Related Studies:
Discipline:
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About the Data
Data Description: The study examines 80 source covers, each performed by two separate artists for a total of 160 songs. For each song, the similarity of specific audio features were analysed using a stemming process, resulting in four constituent components.
TK Dataset: https://labrosa.ee.columbia.edu/projects/coversongs/covers80/
Data Type: Primary and Secondary data
Secondary Data Sources:
Data Collection Methods:
Data Analysis Methods:
Industry(ies):
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Cross Country Study?: No
Comparative Study?: No
Literature review?: No
Government or policy study?: No
Time Period(s) of Collection:
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Abstract

“Music is incorporated into our daily lives whether intentional or unintentional. It evokes responses and behavior so much so there is an entire study dedicated to the psychology of music. Music creates the mood for dancing, exercising, creative thought or even relaxation. It is a powerful tool that can be used in various venues and through advertisements to influence and guide human reactions. Music is also often “borrowed” in the industry today. The practices of sampling and remixing music in the digital age have made cover song identification an active area of research. While most of this research is focused on search and recommendation systems, plagiarism is a real industry wide problem for artists today. Our research seeks to describe a framework of feature analysis to improve cross-similarity, song-to-song, similarity distance measurements. We do this with the context that cover songs represent a fertile training ground to prove methods that can later be applied to plagiarism use cases. Our proposed method preprocesses songs by first source separating the songs into its constituent tracks prior to feature generation. This is otherwise known as “stemming”. These subsequent spectral and distance features are then analyzed to provide evidence of improvement in overall modeling and detection performance. We find that using stem distances and overall distance measures achieves an uplift of 61.8% increase in Accuracy, a 59.2% increase in AUC, a 304.7% increase in Precision, and a 105.1% increase in F1 score for a regularized logistic regression. This process can be directly applied to more sophisticated deep learning frameworks”

Main Results of the Study

The study finds that whilst overall similarity (and vocalisations) of a cover song may be close when analysed using an algorithm, other more granular ‘stems’ are able to detect greater distance between perceived similarities (for example, in regards bass, drums, pitch etc.).

The methods explored in this article can potentially be used to detect song plagiarism. When analysing a sub-sample of the dataset of 46 songs, where plagiarism had previously been confirmed whether through court decisions or subsequent payment of damages, the final version of the stem model was able to confirm the same in 47.5% of cases. As such, this may be a step towards building an automated plagiarism detection system.

Policy Implications as Stated By Author

Whilst the study does not offer any explicit policy recommendations, the researchers advocate the inclusion of cover song detection algorithms to identify cases of plagiarism and infringement. However, they caution that this should be developed to aid individual artists, rather than large companies.



Coverage of Study

Coverage of Fundamental Issues
Issue Included within Study
Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare
Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)?
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)
Green-tick.png
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)
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

Sample size: 160
Level of aggregation: Songs
Period of material under study: