Two Data Sets for Tempo Estimation and Key Detection in Electronic Dance Music Annotated from User Corrections
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TWO DATA SETS FOR TEMPO ESTIMATION AND KEY DETECTION IN ELECTRONIC DANCE MUSIC ANNOTATED FROM USER CORRECTIONS Peter Knees,1 Angel´ Faraldo,2 Perfecto Herrera,2 Richard Vogl,1 Sebastian Bock,¨ 1 Florian Horschl¨ ager,¨ 1 Mickael Le Goff 3 1 Department of Computational Perception, Johannes Kepler University, Linz, Austria 2 Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain 3 Native Instruments GmbH, Berlin, Germany [email protected] ABSTRACT forerunner genres, but also most generic and formulaic pop forms, including contemporary rock, r&b and rap music. We present two new data sets for automatic evaluation of In fact, given its spread over millions of followers, EDM tempo estimation and key detection algorithms. In con- is a central element in the 21st century’s popular music — trast to existing collections, both released data sets focus and therefore a major economical factor in the entertain- on electronic dance music (EDM). The data sets have been ment industry. 1 2 3 automatically created from user feedback and annotations Despite its popularity, in terms of musical sophistica- extracted from web sources. More precisely, we utilize tion, the reputation of EDM might not be the best: “sim- user corrections submitted to an online forum to report plistic,” “too repetitive,” “feasible with lack of talent,” Beatport wrong tempo and key annotations on the website. “fake music,” or “button-pushing” are some of the crit- Beatport is a digital record store targeted at DJs and focus- icisms we can find in press, social media, or even in ing on EDM genres. For all annotated tracks in the data academia. In contrast to such stereotyped views, for MIR sets, samples of at least one-minute-length can be freely research, EDM, in fact, presents an interesting area as downloaded. For key detection, further ground truth is ex- some styles have inherent properties that may challenge Beat- tracted from expert annotations manually assigned to or pose difficult problems for existing music description port tracks for benchmarking purposes. The set for tempo algorithms. These properties include complex rhythm pat- estimation comprises 664 tracks and the set for key detec- terns (as can be observed in IDM or breakbeat), tonal pat- tion 604 tracks. We detail the creation process of both data terns beyond major-minor distinctions [41], structural de- sets and perform extensive benchmarks using state-of-the- velopment not using intro-verse-chorus, temporal devel- art algorithms from both academic research and commer- opments simply based on reoccurring tension-relaxation cial products. patterns (such as “drops” [1, 43]), or, contrarily, develop- ments that are not built on tension-relaxation schemes at 1. INTRODUCTION all. This has been acknowledged by musicologists and the- Electronic dance music (EDM) is one of the most im- orists [8, 19, 40, 41, 44]. portant and influential music genres of our time. The Although some work on topics pertinent to electronic genre has been defined as a broad category of popu- music, e.g., regarding timbre, rhythm, segmentation, or in- lar music that, since the end of the 1990s, encompasses dividual sub genres, have been published in recent years styles such as techno, house, trance, and dubstep, and, [1,10,12,17,18,26,29–31,33,35,42,43], and there seems uniquely, utilizes electronic instruments such as synthesiz- to be a trend towards tempo estimation, e.g., [20, 28], we ers, drum machines, sequencers, and samplers. Tradition- still lack EDM-specific annotated collections and data sets. ally, technologically-mediated live performances form an For instance, existing data sets for tempo (or beat) esti- integral part of EDM [6, 8]. mation comprise of ballroom dance genres [23], Beatles Historically, EDM evolved from and links genres from tracks [13, 25], classical, jazz, and (J-)pop [22], rock/pop, the 1950s to the 1980s such as soul, funk, disco, rap, and dance, classical, folk and jazz [24], or examples from clas- techno. After two decades of isolation as a genre, today, sical music, romantic music, film soundtracks, blues, chan- we are witnessing how it not only influences its legitimate son, and solo guitar tracks selected for “difficulty” [27]. Similarly, for tonality-related tasks, existing data sets com- prise of tracks by The Beatles and Queen [32], Robbie c Peter Knees, Angel´ Faraldo, Perfecto Herrera, Richard Williams [16], piano chords [2], and rock and pop mu- Vogl, Sebastian Bock,¨ Florian Horschl¨ ager,¨ Mickael Le Goff. Licensed under a Creative Commons Attribution 4.0 International Li- 1 http://www.amsterdam-dance-event.nl/static/files/dance- cense (CC BY 4.0). Attribution: Peter Knees, Angel´ Faraldo, Per- onomics economic-significance-edm-17102012.pdf fecto Herrera, Richard Vogl, Sebastian Bock,¨ Florian Horschl¨ ager,¨ Mick- 2 http://www.thembj.org/2013/12/the-economics-of-the-electronic- ael Le Goff. “Two data sets for tempo estimation and key detection dance-industry/ in electronic dance music annotated from user corrections”, 16th Interna- 3 https://smartasset.com/insights/the-economics-of-electronic-dance- tional Society for Music Information Retrieval Conference, 2015. music-festivals 364 Proceedings of the 16th ISMIR Conference, M´alaga, Spain, October 26-30, 2015 365 sic [7, 15]. Other data sets used in MIR research that con- “93 bpm not 111 or whatever it is!” tain electronic dance music or other types of electronic mu- “bpm is 120 not 160. i should know, i made it ;)” sic, such as the Million Song Dataset [3], the MediaEval “173 bpm / g minor” 4 2014 Crowdsourcing Task data set, or the art-oriented “key should be c# minor” UbuWeb corpus [11], lack human annotations of tempo “wrong bpm” and key, among others. “the bpm is fine... its the genre. it’s progressive house, not In this paper, we want to address this lack of EDM data tech house.” sets for MIR research. To this end, we propose two data sets – one for the task of tempo estimation and one for the Table 1. Examples of correctional comments published on task of key detection. In contrast to existing collections, the online forum (links to tracks removed for readability) both released data sets focus on electronic dance music. Since labeling a corpus manually is a labor-intense task, we follow another strategy to obtain human ground truth heterogeneous and in many cases incomplete (no informa- 7 annotations for tracks from an digital online record store tion, reference to track missing, etc.) Nonetheless, as focusing on EDM, namely Beatport. 5 As tempo and key other work has shown [37], online forums present a great information given by the retailer are imperfect, users were opportunity to extract user-generated, music-related infor- encouraged to give feedback on spotted incorrect data us- mation. Table 1 shows typical comments posted into the ing a dedicated online forum. We describe this forum in forum. Section 2. We extract the contained information using We performed a complete web crawl of this user fo- regular expressions and knowledge-based filtering in or- rum in May 2014. At the time of the crawl, there were der to obtain user-based annotations for the corresponding 2,412 comments available, of which 1,857 contained a di- tracks (Section 3). In Section 4, we present some descrip- rect link to a track on the Beatport website. From the link tive statistics on the extracted ground truth. Section 5 re- to the track, we download the complete meta-data record ports on benchmarking results obtained using a variety of in JSON format using web scraping techniques. From this, academic and commercial algorithms on the two new data we also extract the associated style descriptor for statistical sets. We conclude this paper by discussing the modalities reasons, cf. Section 4. of making this data set available to the research community and by drawing conclusions in Section 6. 3. GROUND TRUTH EXTRACTION In this section we detail the process of extracting ground 2. BEATPORT USER FORUM truth from the 1,857 comments that contained a link to a Beatport is a US- and Germany-based online music store track. First, we describe the process of extracting BPM targeted at DJs and music producers. In comparison (beats-per-minute) information. Second, we describe the to standard music web stores, it emphasizes additional extraction of key information from the forum, as well as meta-data relevant for DJs, such as tempo, key, and style, from expert sources available online. All steps were per- as well as information on record label, release information, formed after case-folding the texts. version, and remixing artists, making it an interesting source for MIR research. Meta-data associated with a track 3.1 BPM Extraction can be easily extracted in JSON format from the source For BPM extraction, we retain all posts that contain the code of the corresponding web page. This meta-data also word ‘bpm’ and a two- or three-digit number, option- contains links to the listening snippets of the tracks, which ally followed by a decimal point and a one- to three-digit are typically between 60 and 120 seconds long. number. On the remaining posts, we apply several rule- An important observation is that tempo and key infor- based filter criteria to exclude unlikely or possibly unre- mation provided on the website are determined algorith- lated numbers. This comprises of all numbers below 40 mically upon upload of the tracks by undisclosed algo- and above 250 as these represent tempo values with a low rithms. Thus, this information can not be considered a probability of occurrence in this context. Furthermore, we ground truth and is therefore useless for evaluation pur- remove all two- or three-digit numbers (with optional dec- 6 poses.