EXPLORING MOOD METADATA: RELATIONSHIPS WITH GENRE, ARTIST AND USAGE METADATA Xiao Hu J. Stephen Downie International Music Information Retrieval Systems Evaluation Laboratory The Graduate School of Library and Information Science University of Illinois at Urbana-Champaign {xiaohu, jdownie}@uiuc.edu ABSTRACT evidenced by the ongoing discussions to establish a “Audio Mood Classification” (AMC) task at the Music There is a growing interest in developing and then Information Retrieval Evaluation eXchange (MIREX) 1 evaluating Music Information Retrieval (MIR) systems [3], this lack of common understanding is inhibiting that can provide automated access to the mood progress in developing and evaluating mood-related dimension of music. Mood as a music access feature, access mechanisms. In fact, it was the MIREX however, is not well understood in that the terms used to discussions that inspired this study. Thus, this paper is describe it are not standardized and their application can intended to contribute our general understanding of be highly idiosyncratic. To better understand how we music mood issues by formally exploring the might develop methods for comprehensively developing relationships between: 1) mood and genre; 2) mood and and formally evaluating useful automated mood access artist; and, 3) mood and recommended usage (see techniques, we explore the relationships that mood has below). It is also intended to contribute more with genre, artist and usage metadata. Statistical analyses specifically to the MIREX community by providing of term interactions across three metadata collections recommendations on how to proceed in constructing a AllMusicGuide.com epinions.com Last.fm ( , and ) possible method for conducting an “AMC” task. reveal important consistencies within the genre-mood Our primary dataset is derived from metadata found and artist-mood relationships. These consistencies lead within the AllMusicGuide.com (AMG) site, a popular us to recommend a cluster-based approach that music database that provides professional reviews and overcomes specific term-related problems by creating a metadata for albums, songs and artists. Secondary data relatively small set of data-derived “mood spaces” that sets were derived from epinions.com and Last.fm , could form the ground-truth for a proposed MIREX themselves both popular music information services. “Automated Mood Classification” task. The fact that real world users engage with these services allows us to ground our analyses and conclusions within 1 INTRODUCTION realistic social contexts of music seeking and consumption. 1.1 Music Moods and MIR Development In a previous study [5], we examined a relatively In music psychology and education, the emotional novel music metadata type: “recommended usage”. We component of music has been recognized as the most explored the relationships between usages and genres as strongly associated with music expressivity [6]. Music well as usages and artists using a set of 11 user information behaviour studies (e.g., [10]) have also recommended usages provided by epinons.com , a identified music mood as an important criterion used by website specializing in product reviews written by people in music seeking and organization. Several customers. Because both music moods and usages experiments have been conducted to classify music by involve subjective reflections on music, they can vary mood (e.g., [7][8][9]). However, a consistent and greatly both among, and within, individuals. It is comprehensive understanding of the implications, therefore interesting to see whether there is any stable opportunities and impacts of music mood as both relationship between these two metadata types. We metadata and content-based access points still eludes the explore this question by examining the set of albums MIR community. Since mood is a very subjective notion, common to the AMG mood dataset and our there has yet to emerge a generally accepted mood epinions.com usage dataset [5]. taxonomy that is used within the MIR research and The rest of the paper is organized as follows: Section development community. For example, each of 2 describes how we derived the mood categories used in aforementioned studies used different mood categories, the analyses. Sampling and testing method is described making meaningful comparisons between them difficult. in Section 3. Sections 4 to 6 report analyses of the Notwithstanding that there is a growing interest in relationships between mood and genre, artist and usage tackling mood issues in the MIR community--as respectively. In Section 7, the results from Sections 4-6 © 2007 Austrian Computer Society (OCG). 1 http://music-ir.org/mirexwiki undergo a corroboration analysis using an independent columns) as the similarity measure between each pair of dataset from Last.fm . Section 8 concludes the paper mood labels. Second, an agglomerative hierarchical and provides recommendations for a possible MIREX clustering procedure using Ward’s criterion [1] was “Audio Mood Classification” task. applied to the similarity data. Third, the resultant two cluster sets (derived from album-mood and song-mood 2 MOOD CATEGORIES pairs respectively) were examined and found to have 29 mood labels out of the original 40 that were consistently 2.1 Mood Labels on AMG grouped into 5 clusters at a similar distance level. Table 1 presents the resultant 5 mood clusters along with their AMG claims to be “the most comprehensive music 1 constituent mood terms ranked by the number of reference source on the planet” and supports access to associated albums. music information by mood label. There are 179 mood labels in AMG where moods are defined as “adjectives Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 that describe the sound and feel of a song, album, or Rowdy Amiable/ Literate Witty Volatile overall body of work” 2 and include such terms as Rousing Good natured Wistful Humorous Fiery “happy”, “sad”, “aggressive”, “stylish”, “cheerful”, etc. Confident Sweet Bittersweet Whimsical Visceral These mood labels are created and assigned to music Boisterous Fun Autumnal Wry Aggressive works by professional editors. Each mood label has its Passionate Rollicking Brooding Campy Tense/anxious own list of representative “Top Albums” and its own list Cheerful Poignant Quirky Intense of “Top Songs”. The distribution of albums and songs Silly across these mood lists is very uneven. Some moods are Table 1. Popular Set mood label clustering results associated with more than 100 albums and songs while others have as few as 3 albums or songs. This creates a Note the high level of synonymy within each cluster data sparseness problem when analysing all 179 mood and the low level of synonymy across the clusters. This labels. To alleviate this problem, we designed three state of affairs suggests that the clusters are both alternative AMG datasets: reasonable and potentially useful. The high level of synonymy found within each cluster helps to define and 1. Whole Set : Comprises the entire 179 AMG mood clarify the nature of the mood being captured better than label set. Its “Top Album” lists include 7134 album- a single term label could (i.e., lessens ambiguity). For mood pairs. Its “Top Song” lists include 8288 song- this reason, we are NOT going to assign a term label to mood pairs. any of these clusters in order to stress that the “mood 2. Popular Set : Comprises those moods associated with spaces” associated with each cluster is really the more than 50 albums and 50 songs. This resulted in aggregation of the mood terms represented within each 40 mood labels and 2748 album-mood and 3260 column. song-mood pairs. 3 SAMPLING AND TESTING METHOD 3. Cluster Set : Many albums and songs appear in multiple mood label lists. This overlap can be In each of the following sections, we analyse the exploited to group similar mood labels into several relationship of mood to genre, artist and usage using our mood clusters. Clustering condenses the data three datasets. We focus on the “Top Album” lists from distribution and gives us a more concise, higher- each of these sets rather than their “Top Song” lists level view of the mood “space”. The set of albums because the album is the unit of analysis on and songs assigned to the mood labels in the mood epinions.com to which we will turn in Section 6 when clusters forms our third dataset (described below). looking at usage-mood interactions. At the heads of Sections 4-6, you will find 2.2 Mood Clustering on Top Albums and Top Songs information about the specific (and slightly varying) In order to obtain robust and more meaningful clustering sampling methods used for each of the relationships results, it is advantageous to use more than one view of explored. In general, the procedure is one of gathering the available data. The AMG dataset provides two views: up the albums associated with a set of mood labels and “Top Albums” and “Top Songs”. Thus, we performed their genre, artist or usage information and then the following clustering methods independently on both counting the number of [genre|artist|usage]-mood label the “Top Albums” and the “Top Songs” mood list data pairs that occur for each album. The overall sample of the Popular Set . space is the total number of [genre|artist|usage]-mood First, a co-occurrence matrix was formed such that label pairs across all relevant albums. each cell of the matrix was the number of albums (or To test for significant [genre|artist|usage]-mood label songs) shared by two of the 40 “popular” mood labels pairs, we chose the Fisher’s Exact Test (FET) [2]. FET specified by the coordinates of the cell. Pearson’s is used to examine the significance of the correlation was calculated for each pair of rows (or association/dependency between two variables (in our case [genre|artist|usage]-mood), regardless of whether the sample sizes are small, or the data are very 1 AllMusicGuide.com : “About Us”.