Music distribution applications

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MIR is fundamental for developing technologies to be used in the music distribution ecosystem. The stakeholders in the music value chain are music services, record companies, performing rights organisations, music tech companies, music device and equipment manufacturers, and mobile carriers. The main challenge is to develop scalable technologies that are relevant to both the services that organise and distribute the music and also those services that track what is being distributed. These technologies span from music search and recommendation to audio identification both for recordings and compositions among others. By fully addressing the music distribution challenges, the MIR Community will establish closer ties with the industry which will help accessing resources (such as actual music data) and alternative ways of funding. On its side, the Music Distribution industry will have access to technologies more targeted to actual end-user scenarios which will give them an edge in the global market. Incidentally, it will help reducing innovation cycles from research to development and exploitation which, in turn, will have a clear impact on competitiveness and hence music distribution companies’ profitability.


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Contents

State of the art

A number of topics on the future of electronic music distribution have been addressed. This includes music search and discovery of music catalogues, the music rights industry-related technologies and other more transversal topics such as scalability and metadata cleaning.

As could be witnessed over the last few years, music is being produced and published at a faster rate than ever before: estimates range form yearly 11,000 (nonclassical) major label albums averaging some ten songs per album [Vogel, 2010] up to 97,751 albums released in the United States in 2009, as reported by Nielsen SoundScan.

In the physical world, record shops were de-facto intermediaries that preselected music due to the physical constraints of storing music records and cd's. Digital technologies have changed this situation in at least two respects: digital music distribution channels such as iTunes, Amazon or Spotify can provide quick access to millions of music pieces at very low cost, hence they are less strictly preselected, and, with the abandonment of physical records, they shifted granularity from albums to single tracks, making it even harder for potential customers to make a choice. To fill this gap of missing preselections, automatic music recommendation systems supporting search and discovery have been developed attempting to provide an improved and manageable access to the music of the world.

Amazon suggests albums or songs based on what has been purchased in the same order or by the same customers as items one searched for or bought. This is a form of collaborative filtering [Herlocker et al., 1999], which assumes that users who have agreed in the past (in their purchase decisions) will also agree in the future (by purchasing the same items). Collaborative filtering generally suffers from two related problems: the coldstart problem and the popularity bias. The coldstart problem is the fact that albums that have not yet been purchased by anybody can never be suggested. The popularity bias is the problem that for any given item, popular albums are more likely to have been purchased in conjunction with it than unpopular ones, and so have a better chance of being recommended. In consequence, collaborative filtering alone is incapable of suggesting new music releases. An additional problem specific to Amazon is that users may purchase items for somebody else (e.g., as a present), which might flaw the recommendations generated both for them and for other users of allegedly the same taste. Spotify, a music streaming service, bases its recommendations (see also Erik Bern) on its users' listening behavior, analysing which artists are often played by the same listeners. While this may potentially result in better suggestions than analysing sparse data such as record purchases, it is again subject to the cold-start problem and popularity bias. Furthermore, Spotify only recommends related artists and not songs, which is rather unspecific. Genius is a function in Apple iTunes which generates playlists and song recommendations by comparing music libraries, purchase histories and playlists of all its users, possibly integrating external sources of information. Assuming such external information does not play a major role, this system is again based mainly on collaborative filtering. Last.fm combines information obtained from users' listening behavior and user-supplied tags (words or short expressions describing a song or artist). Tags can help to make recommendations transparent to users, e.g. a user listening to a love song may be recommended other tracks that have frequently been tagged as 'slow' and 'romantic'. But they are also inherently erroneous due to the lack of carefulness of some users, and require a range of counter measures for data cleaning. Tags are also affected by the cold-start problem and popularity bias. Pandora, another music streaming service, recommends songs from its catalogue based on expert reviews of tracks with respect to a few hundred genre-specific criteria. This allows for very accurate suggestions of songs that sound similar to what a user listens to, including sophisticated explanations for why a song was suggested (e.g., a track may be recommended because it is in a 'major key', features 'acoustic rhythm guitars', 'a subtle use of vocal harmony' and exhibits 'punk influences'). Such expert reviews incur high costs in terms of time and money which makes it impossible to extend the catalogue at a rate that can keep up with new releases. This has a limiting effect on the selection of music available to users.

Most approaches described so far rely on some form of meta-information: user's listening or purchasing behavior, statistics about artists and genres in music collections, user defined tags etc. Another option is to actually analyse the audio content trying to model what is important for the perceived similarity between songs: instrumentation, tempo, rhythm, melody, harmony, etc. While many research prototypes of recommendation systems that use content-based audio similarity have been described in the literature [e.g. Pampalk, 2001;, Neumayer et al., 2005; Lamere and Eck, 2007; Knees et al., 2007; to name just a few], very little has been reported about successful adoption of such approaches- without combination with other methods- to real-life scenarios. Content based recommendation is used to some extent by a number of music companies like Mufin, echonest or BMAT amongst others. An exhaustive view on Music Recommendation systems can be found at [Celma, 2010].

In a landscape where the music industry is facing difficult times with income from physical sales shrinking, the music rights revenues are increasing worldwide. According to CisacPortal the author’s society royalty collections were 7.5€ billion in 2010 (climbing a 5,5% year-on-year) and [IFPI, 2012] announced that the global performance rights reached the 905 US$ millions in 2011 (an increase of 4,9% from the previous year). These positive numbers are due to the increase of the number of media paying royalties and an improvement of the collecting methods of these societies. Hence, it is important to address the needs of the music rights business; i.e. the process of paying the owners of these rights (authors, performers, labels…) for the usage of the music they have created and performed (licensing FAQ).

The rights organisations get most of their revenue not only from television, radio stations and those industries whose services are based on music, like clubs or venues, but also from a lot of other companies and associations from shops or dentists to school plays, basically anyone who aims at using somebody else’s music creation (paying royalties). In recent years, the music rights revenues coming from the digital world have also grown in importance. All this rights money is collected through the royalty collection societies, which are divided in three kinds depending on the rights they represent: Authors, Performance and Master. Most of authors’ societies worldwide are associated with the CISAC while the master societies are associated with the IFPI. The societies collect music rights and distribute them among their associates. At this point, a lot of controversy arises due to the different processes they use for such distribution and questions are raised about how to make this process as fair as possible (more).

Ideally, every right owner should be paid for the use of their music but in practice it is difficult and expensive to control all the media and all potential venues where music could eventually be used. The solutions that have been found vary depending on the country, the society and the type of source. Some years ago, the societies used to distribute based on the results of the top selling charts which created huge inequalities between artists. Later some other systems and technologies appeared:

While the research and engineering problems of simple audio identification use cases have practically been solved; for other real industry use cases, such as background music detection (over voice), in noisy backgrounds and with edited music, there are no robust technical solutions. In this business niche, a number of players share the market: Tunesat in the USA, BMAT in Spain, kollector in Europe, Monitec in Southamerica, Soundmouse in the UK and yacast in France.

A major challenge a new technology must face when it is to be applied in viable commercial products is scalability; i.e. the ability of the technology to handle massive amounts of data and the ability to handle that data’s eventual growth in a cost effective manner. The problem is twofold. Firstly, some techniques are simply neither deployed nor tested since it’s computationally impossible due to the size of datasets. Secondly, assuming the technique is scalable from a non-functional point of view, applying it to multi-million datasets may reveal problems which were not obvious in the first place. Beyond the problem of handling "big data", granting research access to huge music-related datasets may generate beneficial by-products for the music information research world. First, in large collections, certain phenomena may become discernible and lead to novel discoveries. Secondly, a large dataset can be relatively comprehensive, encompassing various more specialised subsets. By having all subsets within a single universe, we can have standardised data fields, features, etc. Lastly, a big dataset available to academia greatly promotes the interchange of ideas and results leading to, yet again, novel discoveries. A good example here is the "Million Songs Dataset" [Bertin-Mahieux et al., 2011], which contains user tags provided by Last.Fm.

Systems that are able to automatically recommend music (as described above) are one of the most commercially relevant outcomes from the MIR community. For such recommender systems it is especially important to be able to cope with very large - and growing - collections of music. The core technique driving automatic music recommendation systems is the modelling of music similarity which is one of the central notions of MIR. Proper modelling of music similarity is at the heart of every application allowing automatic organisation and processing of music databases. Scaling up sublinearly the computation of music similarity to the millions is therefore an essential concern of MIR. Scalable music recommendation systems have been the subject of a number of publications. Probably one of the first content-based music recommendation systems working on large collections (over 200.000 songs) was published by [Cano et al., 2005]. Although latest results [see e.g. Schnitzer et al., 2012] enable systems to answer music similarity queries in about half a second on a standard desktop CPU on a collection of 2.5 million music tracks yet, the system performs in a linear fashion.

The issue of scalability clearly also affects other areas of MIR: music identification meaning both pure fingerprinting technologies and cover detection, multimodal music recommendation and personalisation (using contextual and collaborative filtering Information).


References

Challenges



Back to → Roadmap:Exploitation perspective

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