A professionally annotated and enriched multimodal data set on popular music

Abstract

This paper presents the MusiClef data set, a multimodal data set of professionally annotated music. It includes editorial metadata about songs, albums, and artists, as well as MusicBrainz identifiers to facilitate linking to other data sets. In addition, several state-of-the-art audio features are provided. Different sets of annotations and music context data -collaboratively generated user tags, web pages about artists and albums, and the annotation labels provided by music experts -are included too. Versions of this data set were used in the MusiClef evaluation campaigns in 2011 and 2012 for auto-tagging tasks. We report on the motivation for the data set, on its composition, on related sets, and on the evaluation campaigns in which versions of the set were already used. These campaigns likewise represent one use case, i.e. music autotagging, of the data set. The complete data set is publicly available for download at

Music Mood Dataset Creation Based on Last FM Tags

“…Word cloud of emotional tags is presented in Figure 4. Moving on with data processing, we kept only tags assigned to at least 20 songs, same as in [26]. We removed tags related to genre (e.g., rock, pop, indie), instrumentation (guitar, electronic), epoch (00s, 90s) or other tags not related to mood.…”

“…However it is very small (100 songs only) and thus cannot be used as a serious experimentation set. In [26] the authors describe Musiclef, a professionally created multimodal dataset. It contains metadata, audio features, last.fm tags, web pages and expert labels for 1355 popular songs.…”

Crowdsourcing Emotions in Music Domain

“…This is one of the few public lyrics datasets of a reasonable size (771 lyrics) that are available. In [41] authors describe Musiclef, a professionally created multimodal dataset. It contains metadata, audio features, Last.fm tags, web pages and expert labels for 1355 popular songs.…”

“…Last.fm is a popular music community that has rendered public most of its music collections and corresponding user-generated tags [5]. Is is highly popular among researchers who have been experimenting with its tags in several studies [6,7,8,9]. In many cases researchers utilize music tags to build labeled datasets which are essential for training and testing MIR and MER algorithms.…”

The Greek Audio Dataset

“…Schedl et al [15], presented the MusiClef dataset, a multimodal data set of professionally annotated music. MusiClef contains editorial meta-data, audio features (generic and MIR oriented) are provided.…”