Empirical Analysis of Track Selection and Ordering in Electronic Dance Music Using Audio Feature Extraction

EMPIRICAL ANALYSIS OF TRACK SELECTION AND ORDERING IN ELECTRONIC DANCE MUSIC USING AUDIO FEATURE EXTRACTION Author1 Author2 Retain these fake authors in Retain these fake authors in submission to preserve the formatting submission to preserve the formatting ABSTRACT further. We investigate this selection and ordering process in terms of the automatically computed similarity between Disc jockeys are in some ways the ultimate experts at tracks, in terms of features representing timbre, key, tempo selecting and playing recorded music for an audience, es- and loudness. The source data for this investigation is the pecially in the context of dance music. In this work, we British Broadcasting Corporation’s ’Essential Mix’ radio empirically investigate factors affecting track selection and program 1 . Broadcast since 1993, the Essential Mix show- ordering using mixes created for the Essential Mix. The cases exceptional DJs of various genres of electronic dance Essential Mix is a well known weekly radio show on BBC music (EDM), playing for one or two hours. It is consid- Radio 1 that showcases various styles of electronic dance ered one of the most reputable and influential radio pro- music. We use automatic content-based analysis and dis- grams in the world. By investigating the relationships be- cuss the implications of our findings to playlist generation tween tracks in DJ sets we hope to better understand track and ordering. Timbre appears to be an important factor selection by DJs and inform the design of algorithms and when selecting tracks and ordering tracks, and track order audio features for automatic playlisting. itself matters, as shown by statistically significant differ- The automatic estimation of music similarity between ences in the transitions between the original order and a two tracks has been a primary focus of music informa- shuffled version. We also apply this analysis to ordering tion retrieval (MIR) research. Several methods for comput- heuristics and suggest that the standard playlist generation ing music similarity have been proposed based on content- model of returning tracks in order of decreasing similar- analysis, metadata (such as artist similarity, web reviews), ity to the initial track may not be optimal, at least in the and usage information (such as ratings and download pat- context of track ordering for electronic dance music. terns in peer to peer networks). Music similarity is the ba- sis of query-by-example which is a fundamental MIR task, 1. INTRODUCTION and also one of the first tasks explored in MIR literature. In this paradigm the user submits a query consisting of one The invention of recording lead to the possibility of select- or more ’seed” pieces of music, sometimes also including ing recorded music to entertain a group of people. The metadata and user preferences. The system then responds idea of listening to records instead of listening to bands by returning a playlist of music pieces ranked by their sim- took off after the second world war, when sound systems ilarity to the query, and set in some order. In contrast our and record players began to appear in night clubs and cafes approach is analytic. Rather than generating playlists, we in New York, Jamaica, London, Paris, and beyond [5]. investigate existing DJ sets through audio feature extrac- Since then, the disk jockey (DJ) has evolved from a tion and examine the transitions between tracks in terms of simple selector and orderer of music into a sophisticated audio features representing timbre, loudness, tempo, and performer with considerable skill and training. Although key. We also compare the results of our empirical investi- these performance aspects are compelling, the primary fo- gation to common ordering methods, and offer some sug- cus of this paper is the basic selection and ordering of mu- gestions for improving current playlisting heuristics. sic. DJs generally bring a limited amount of their music collection to any given gig, and play a reasonably large 2. RELATED WORK subset of it. Two important questions to consider are ’What tracks go into a playlist?’, and ’What is the best ordering of Early MIR work investigating the automatic calculation of these tracks?’. Track ordering is not a well understood pro- music similarity and how to evaluate different approaches cess, even by DJs. Many DJs will say only that two tracks formulated a general methodology that is followed by the work or do not work together, and not be able to comment majority of existing work to this day. In this methodology, the primary goal is assessing the relative performance of different algorithms for computing music similarity by Permission to make digital or hard copies of all or part of this work for somehow evaluating the ’quality’ of the generated playlists. personal or classroom use is granted without fee provided that copies are The most common approach of generating a playlist is not made or distributed for profit or commercial advantage and that copies to consider the N closest neighbors in terms of automat- bear this notice and the full citation on the first page. c 2012 International Society for Music Information Retrieval. 1 http://www.bbc.co.uk/programmes/b006wkfp ically calculated similarity to a particular query. Several novelty aspect of playlist generation by tracking user lis- automatic playlists are generated from seed queries repre- tening information has also been explored [9]. A different senting the desired diversity of the music considered. This approach altogether is to create playlists visually, based on set of automatically generated playlists is then evaluated, some graphical representation of the music collection [19]. typically using one or both of two approaches: objective In all of this literature, different approaches to playlist gen- evaluation using proxy ground truth for relevance, or sub- eration are also evaluated with a combination of objective jective evaluation through user studies. The basic idea is measures and user studies, comparing different configura- to evaluate a playlist by considering it good if it contains a tions to a random or a simple algorithmic baseline. For high number of ’relevant items” to the query [1]. The rele- example, a recent study compared two recommender sys- vance ground truth can be provided by users in subjective tems (based on artist similarity and acoustic content) with evaluation but this is a time consuming and labor intensive the Apple iTunes genius recommender which is believed process that does not scale well. to be based on collaborative filtering [3]. Objective evaluation has the advantage that it can scale to any number of queries and playlists, as long as the tracks 3. MOTIVATION AND PROBLEM have some associated meta-data that can be used as a proxy FORMULATION for relevance. Common examples of such proxy sources include artist, genre, and song [1,10]. In addition to music The motivation behind our work is to investigate the pro- similarity calculated based on audio content analysis, other cess of playlist/mix creation by analyzing existing mixes sources of information such as web reviews, download pat- created by experts, i.e DJs. Existing work has mostly fo- terns, ratings and explicit editorial artist similarity [7] can cused on more general playlists created by average listen- also be used for estimating music similarity [6]. Playlists ers. Rather than relying on user surveys, we focus on em- themselves have also been used to calculate artist and track pirical analysis based on audio feature extraction. This al- similarity based on co-occurrence. Sources of playlist data lows us to investigate what audio attributes DJs use when include the Art of the Mix, a website that contains a large selecting and order their tracks. We further compare these number of hobbyist playlists [4], and listings of radio sta- attributes to collections of random EDM tracks, and to tions [12]. DJ sets remain an untapped resource, however. artist albums. We specifically investigate whether track order matters. We also examine important assumptions that The most common relevance-based evaluation measures are frequently made by automatic playlist generation sys- (such as precision, recall and F-measure [2]) are borrowed tems. Specifically, we investigate if ordering based on sim- from text information retrieval and only consider the items ilarity ranking is a good choice, and if so, in what manner. contained in the set of returned results, without taking into In existing literature these assumptions are typically man- account their order. The paradigm of a single seed query ifested in the design of an automatic playlist algorithm, song creating a list of N items ranked by similarity has re- and the results are evaluated through objective or subjec- mained a common approach to automatic playlisting and tive approaches. Issues such as the sameness problem in music recommendation. Some notable exceptions in terms playlists formed from collections of music that do not have of ordering include: heuristics about trajectory for the or- stylistic diversity, or the playlist drift problem in large di- dering of returned items [10], using song sets instead of verse collections are also discussed but are not empirically single seeds [11], ordering based on the traveling sales- supported [8]. In contrast, our approach is complimentary man problem [17], and considering both a start track and and attempts to test these assumptions directly on existing an end track for the playlist [8]. The assumption of simi- mixes. Our methodology can be also viewed as an em- larity has also been challenged by the finding that in many pirical musicological approach to understanding how DJs cases users prefer diverse playlists [18] as measured by au- select and order music. tomatic feature analysis. This is the closest work in terms of approach to the work described in this paper. Another theme of more recent work has been providing 4.

Load more