Collecting ground truth annotations for drum detection in polyphonic music

In order to train and test algorithms that can automatically detect drum events in polyphonic music, ground truth data is needed. This paper describes a setup used for gathering manual annotations for 49 real-world music fragments containing different drum event types. Apart from the drum events, the beat was also annotated. The annotators were experienced drummers or percussionists. This paper is primarily aimed towards other drum detection researchers, but might also be of interest to others dealing with automatic music analysis, manual annotation and data gathering. Its purpose is threefold: providing annotation data for algorithm training and evaluation, describing a practical way of setting up a drum annotation task, and reporting issues that came up during the annotation sessions while at the same time providing some thoughts on important points that could be taken into account when setting up similar tasks in the future

Generating Rembrandt: Artificial Intelligence, Copyright, and Accountability in the 3A Era--The Human-like Authors are Already Here- A New Model

Artificial intelligence (AI) systems are creative, unpredictable, independent, autonomous, rational, evolving, capable of data collection, communicative, efficient, accurate, and have free choice among alternatives. Similar to humans, AI systems can autonomously create and generate creative works. The use of AI systems in the production of works, either for personal or manufacturing purposes, has become common in the 3A era of automated, autonomous, and advanced technology. Despite this progress, there is a deep and common concern in modern society that AI technology will become uncontrollable. There is therefore a call for social and legal tools for controlling AI systems’ functions and outcomes. This Article addresses the questions of the copyrightability of artworks generated by AI systems: ownership and accountability. The Article debates who should enjoy the benefits of copyright protection and who should be responsible for the infringement of rights and damages caused by AI systems that independently produce creative works. Subsequently, this Article presents the AI Multi- Player paradigm, arguing against the imposition of these rights and responsibilities on the AI systems themselves or on the different stakeholders, mainly the programmers who develop such systems. Most importantly, this Article proposes the adoption of a new model of accountability for works generated by AI systems: the AI Work Made for Hire (WMFH) model, which views the AI system as a creative employee or independent contractor of the user. Under this proposed model, ownership, control, and responsibility would be imposed on the humans or legal entities that use AI systems and enjoy its benefits. This model accurately reflects the human-like features of AI systems; it is justified by the theories behind copyright protection; and it serves as a practical solution to assuage the fears behind AI systems. In addition, this model unveils the powers behind the operation of AI systems; hence, it efficiently imposes accountability on clearly identifiable persons or legal entities. Since AI systems are copyrightable algorithms, this Article reflects on the accountability for AI systems in other legal regimes, such as tort or criminal law and in various industries using these systems

Money for Something: Music Licensing in the 21st Century

[Excerpt] The laws that determine who pays whom in the digital world were written, by and large, at a time when music was primarily performed via radio broadcasts or distributed through physical media (such as sheet music and phonograph records), and when each of these forms of music delivery represented a distinct channel with unique characteristics. With the emergence of the Internet, Congress updated some copyright laws in the 1990s. It applied one set of legal provisions to digital services it viewed as akin to radio broadcasts and another set to digital services it viewed as akin to physical media. Since that time consumers have increasingly been consuming music via digital services that incorporate attributes of both radio and physical media. However, companies that compete in enabling consumers to access music may face very different costs to license music, depending on the technology they use and the features they offer. These differences in technology and features also affect the amount of money received by songwriters, performers, music publishers, and record companies. U.S. copyright law allows performers and record labels to collectively designate an agent to receive payments and to negotiate the licensing fees that certain types of digital music services must pay to stream music to their customers. Groups representing public radio and educational stations reached voluntary agreements with the agent, SoundExchange, in 2015. Rates paid by parties that do not reach voluntary agreements with SoundExchange during a limited negotiation period are instead set by the Copyright Royalty Board (CRB), a panel of three judges appointed by the Librarian of Congress. On December 16, 2015, the CRB set rates for online music streaming services for the period 2016 through 2020. For nonsubscription services, the CRB reduced the per-stream rate it had set in the previous rate proceeding, but the costs paid by several “small” music streaming services are likely to increase. Advocates of the small streaming services have launched a petition asking Congress to either allow their previous agreements to continue indefinitely or discontinue the requirement that small streaming services pay royalties to performers and record labels. SoundExchange has objected that the rates set by the CRB do not provide adequate compensation to performers and record labels. Members have introduced several bills in the 114th Congress that would change the amounts various participants in the music industry pay or receive in royalties. These bills are controversial, as they could alter the cost structures and revenues of broadcast radio stations, songwriters, performers, and others at a time when the music industry’s overall revenues are not growing. At the same time, the U.S. Department of Justice (DOJ) is continuing a review of consent decrees it entered into with music publishers in the 1940s. The outcome could affect the extent to which songwriters can control the use of their works

“It’s Been a Hard Day’s Night” for Songwriters: Why the ASCAP and BMI Consent Decrees Must Undergo Reform

In order to guarantee reasonable fees for songwriters, composers, and publishers, the consent decrees must undergo critical reform to account for how music is licensed in new media. Part I of this Note will provide background on the mechanics of music licensing, both traditional and through modern mediums, in order to explain why the two largest PROs initially entered into governmental consent decrees. Part II will discuss recent judicial determinations of “reasonable” licensing rates for public performances in new media and demonstrate the discrepancy in compensation between songwriters and their sound recording counterparts, namely record companies and recording artists. Finally, Part III will argue that the solution to this problem is through consent decree reform. The decrees should be modified to allow songwriters to withdraw their digital rights in order to separately license songs in new media. A new PRO should then emerge in the market place to account solely for public performance rights in new media, leaving traditional licensing to the existing PROs. Additionally, the current judicial process for setting rates, known as the “rate court” system, should be replaced with expedited, binding arbitration. Making these important changes to the music-licensing system will work towards bridging the gap in compensation inequality between songwriters and recording artists

Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models