Introduction
Intellectual Property or IP, refers to creations made by humans that are under legal protection. This is done to ensure that the appropriate rights holders are able to to control the distribution of IP protected material and the profit generated from it. There are several different types of IP protection in the music industry such as copyright, providing rights holders exclusive privilege in reproduction, distribution, performance, and display of material; trademark to protect names, brands, and logos associated with musicians and record companies; and licensing to regulate usage of copyrighted material to other parties. These laws exist to ensure protection and monetization of musicians’ works across various aspects of the industry(Yellowbrick, 2020)
The focus of this paper will be discussion of copyright protection laws on IPs . However, there are two types of copyright for any given song; compositional copyright and master recording. Compositional copyright refers to legal protection of the musical structures of a song such as tune, chord progressions, harmonies, and lyrics. While master recording copyright is the legal protection of the single song recording and the sounds used in that recording. For the purpose of this research paper, the copyright in question will be compositional copyright as most prolific and publicly debated cases stem from disputes over compositional aspects of music. The most recent of such high-profile cases is the dispute of Ed Sheeran’s “Thinking Out Loud”(2014) infringing Marvin Gaye’s “Let’s Get it On”(1973)—the case starting on September 29, 2022 and ending on May 16, 2023. Which utilizes a methodology very subjective in nature—raising potential for cultural and personal bias highlighted by experiences of the factfinders.
Thus, introducing machine learning models can be used to detect similarities between songs in order to form a more objective approach when handling music copyright cases. Such models when used in practice have potential to bring forth better objectivity to the case proceedings. Eliminating potential bias and subjective nature of traditional methodology by using highly extensive data sets and problem-solving abilities that transcends humans. This technology has a large impact on court verdicts. While no model as such exists at this point in time, this research paper will look to discuss the extent to which these models’ potential utility refine current methodologies used in practice
Literature Review
To first understand the overview of the subject the source “Understanding Intellectual Property in Music: A Comprehensive Overview.”, allows readers to conceptualize several IP protection laws that exist to protect IPs of musicians and all parties in the production of a song or album(Yellowbrick, 2020).
The source, “Copyright Infringement of Music: Determining Whether What Sounds Alike Is Alike”, specifically delves into the laws of copyright. It questions the current copyright laws that are in place for the recordings of music and explores the impact of these laws on the incentive to create, protect rights, and dismantle monopolies of our shared heritage. After the discussion of the above aspects, the article comes to the conclusion that policies should enforce justice on individuals whose music bears “striking similarity” to another. Thus, preserving the care-free creation of music and protecting individuals’ IP(Livingston, 2013). However, the sources’ proposed methodology is prone to subjectivity from factfinders in court.
This is where the implications of the paper “Predictive Models for Music” which itself explores the mechanisms by which melodic structures and tonalities are able to be constructed by utilizing machine learning models, prospes a refined methodology. Breaking down the task into two parts; one to analyze the rhythms of a song and the other, being a generative model, analyzes chord progressions and sequences. Thus, implications of this paper are that a similar model can theoretically be constructed to be used to analyze the similarities between two songs and by bearing a close enough similarity within rhythms and chords, copyright laws can be enforced accordingly(Paiement, 2009).
A model that would have been useful in the dispute of compositional rights of “Thinking Out Loud” by Ed Sheeran violating “Let’s Get It On” by Marvin Gaye. The source had summarized the key events and proceeding of the entire case, including the methodology they had used in the case which involved factfinders to piece together unprotected elements of the plaintiff's song and find similarities in authorship. Hence, being highly subjective in nature(Gosling, 2023).
This subjective nature may also stem from an individual’s own experiences in terms of culture and understanding may alter the emotional or aesthetic perception of the song to factfinders. Thus, resulting in cultural and perception bias given by the source “The Dock-in Model of Music Culture and Cross-Cultural Perception.”(Fritz, 2013).
To bring forth objectivity, innovations in deep-learning models have been made such as continuously updating model of CRNN(Convolutional Recurrent Neural Network), a hybrid of CNN(Convolutional Recurrent Neural Network) and RNN(Recurrent Neural Network), an AI model that has spatial and sequential pattern recognition capabilities. In context of this research such models will allow the analyzing of sheet music and audio drafts that musicians had during the writing process as feeding data. Thus, when comparing the two pieces of music, similarities can be highlighted(Choi et al, 2017).
Other technology that could be used to achieve the initial purpose can be seen in the source “Automated Music Recommendations Using Similarity Learning.”. This source had explored a similar methodology used by Paiement–breaking down music into melody and rhythm using machine learning models to analyze both accordingly. However, the source places a greater emphasis on analyzing the similarities between two songs’ melodic components such as harmony, patterns, and the tempo in which it is played. While the intended use of this technology was to provide users with better recommendations on streaming platforms, an extension of this technology can be used to to detect similarities in songs in context of compositional copyright disputes(Burns, 2020).
We can also consider technology that has potential to be used in master recording disputes, such as “Musical Genre Classification of Audio Signals.”. Unlike previous sources, the methodology to highlight similarities in music is done through audio signals. Doing so will not only provide factfinders with genre classification but also realize the frequency structure of melodic components. (Tzanetakis, 2002)
However, mathematical models used in these sources were used by the court of law before. Evidence of their existence can be seen in the source “Court decisions on music plagiarism and the predictive value of similarity algorithms”, where melody similarity algorithms have been used in disputes over compositional and master recording copyrights including Bright Tunes v Harrisongs(1976) and Selle v Gibb(1984). It was the interpretations of the findings of the algorithms that brought human subjectivity seen in other cases previously mentioned.(Müllensiefen, 2009) Thus, highlighting the importance of deep learning models to eliminate potential bias
Despite perceived benefits of this technology–including objectivity and depth of analysis–it has several limitations in terms of how these models are built. As they have potential for dataset bias and ethical implications to arise–the idea for a machine to influence the verdict of a court.
Analysis
The findings of the literature review suggest that deep-learning models have high potential utility. Evidence to support this claim is seen in these models’ capabilities to run extensive calculations in command of its problem solving capabilities. For example, CRNN had shown such heightened capabilities as a result of being a hybrid of two separate neural networks. Thus, establishing these models’ innate problem solving ability.(Choi et al, 2017)
Furthermore, this ability can be stemmed from deep-learning models to process high volumes of data unparalleled than what could be accomplished by human factfinders. It is because of these two distinct capabilities that the application of this technology is likely to be high in the court of law. This can be seen by the need for such abilities in the dispute between Ed Sheeran’s “Thinking Out Loud” and “Let’s Get It On”, by Marvin Gaye, where the defendants main dispute lies in similarities in chord progression and harmonies. This case in particular required the plaintiff, Gaye, to show that there are several properties of unprotected parts of a song that are apparent in both. Thus, compositional copyright infringement laws protect not only the musician's art, but their authorship as well. Requiring models to piece together several data sets that attempt several combinations and permutations in order to bring forth an objective and refined verdict.(Gosling, 2023)
Moreover, as there are no thresholds in the number of appearances of unprotected content on both songs, an extension of this technology can be set and agreed upon by a judge or jury with two parameters: genre and frequency of usage. Genre refers to the “aesthetic” of the song, while frequency range is more quantitative in nature eliminating potential bias that may be seen if a human were delivering judgment.(Gosling, 2017) This phenomena was discussed as part of the literature review–the upbringing of a person affects their perceptual understanding of what music is and what kind of music depicts a particular emotion.(Fritz, 2013) Thus by automating this process using deep-learning models, thresholds can be set dynamically to the nature of each case. Hence, showing potential for high utility.
However, the limitations of these learning models are realized when these models are unique in nature– each model having a unique set of training data. Thus, certain models that may have discrepancies or biased training data will result in poor and potentially biased verdicts being given. Moreover, machine learning models that bridge into AI such as CRNN are not standardized in behavior and output as they behave in accordance to neural networks that are cognitive. While this alone may not degrade the quality of verdicts–referring to minimal bias–lack of standard and consistent responses may lead to adverse effects on the overall outcome of the case. Thus, limiting the utility of these models in court.(Choi et al, 2017)
Despite the apparent limitations when applying such models it is likely the development of systems used in the court of law are ones developed by the country in which the trial is occuring. Therefore, the only way the counterclaim stands is if the country is biased towards either the plaintiff or defendant–instances such as those are rare. Even so, it is important to assume all courts and their respective governments who may utilize such learning models to detect potential compositional copyright infringement are principled. This is done to showcase the extent to which the applications of deep-learning models achieve the highest utility .
Conclusion
In conclusion, through the findings of both the literature review and the analysis of its findings, it is likely the extent to which the learning models will be used in court will be hybrid in nature. Utilizing the subjective intuition of human factfinders and the objective nature of deep-learning models, will provide a balanced set of evidence and verdicts to deliver a comprehensive final judgment.
This particular conclusion is in tandem due to the level of technology and infrastructure available in most developed or developing countries. To effectively put models such as these into practice, standardization of source code and construction methods of these programs must be open source to all interested governments. In addition, the model must have information regarding copyright law in the country that it is being put to use to ensure universal application of the same program. Moreover, the current deep learning models that are able to detect similarities in music are being used in streaming platform services and musicians to make music, but also extend its utility in the court of law by achieving the objective first set out. Thus, creating positive effects on artists’ creative freedom and liberty. However, it would require high capital investment to make such advancements in technology, in an objective to reach full autonomy.
A key limitation of this study is that the development of these models are highly specific to detect similarities in copyright infringement, rather than the other protection laws as discussed in the introduction.
References
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Choi, Keunwoo, et al. “Convolutional Recurrent Neural Networks for Music Classification.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2392–96. IEEE Xplore, https://doi.org/10.1109/ICASSP.2017.7952585. Accessed 7 Sep. 2024.
Fosler-Lussier, Danielle. “Copyright, Surveillance, and the Ownership of Music.” Music on the Move, University of Michigan Press, 2020, pp. 180–201. JSTOR, https://www.jstor.org/stable/10.3998/mpub.9853855.16. Accessed 5 Sep. 2024.
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Müllensiefen, Daniel, and Marc Pendzich. “Court Decisions on Music Plagiarism and the Predictive Value of Similarity Algorithms.” Musicae Scientiae, vol. Discussion Forum 4B, 2009, pp. 257–95. APA PsycNet, https://doi.org/10.1177/102986490901300111. Accessed 7 Sep. 2024.
Paiement, Jean-François, et al. “Predictive Models for Music.” Connection Science, vol. 21, no. 2–3, Sept. 2009, pp. 253–72. tandfonline.com (Atypon), https://doi.org/10.1080/09540090902733806. Accessed 7 Sep. 2024.
Tzanetakis, G., and P. Cook. “Musical Genre Classification of Audio Signals.” IEEE Transactions on Speech and Audio Processing, vol. 10, no. 5, July 2002, pp. 293–302. IEEE Xplore, https://doi.org/10.1109/TSA.2002.800560. Accessed 7 Aug. 2024.
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https://www.yellowbrick.co/blog/entertainment/understanding-intellectual-property-in-music-a-comprehensive-overview#:~:text=Intellectual%20property%20(IP)%20in%20music%20refers%20to%20the%20legal%20protection,%2C%20perform%2C%20and%20display%20works. Accessed 6 Sep. 2024.
