Data Mining and Text Analytics in MusicAudi Sugianto and Nicholas Tawonezvi

Overview

Introduction Building a ground truth set Experiments Results

Introduction

Purpose: Music mood classification through lyric text mining approaches MIR (Music Information Retrieval) Use of Audio Datasets:

AMC (Audio Mood Classification) USPOP, USCRAP, etc.

Use of Social tags from last.fm

Challenges: Natural subjectivity of music Human perspectives on mood

Generating Ground TruthData Collection

Combination of in-house and public audio tracks Collect songs with at least one social tag from last.fm Lyrics can be gathered from mainly Lyricwiki.org.

Use of Lingua to ensure data quality Finalise songs that have both correct lyrics and tags

Generating Ground TruthAlgorithms, Resources and Techniques

WordNet-Affect Used to filter out junk tags Assignment of labels to concepts (emotions, moods, responses)

Use of human expertise to identify mood-related words in the music domain

Affective Aspect Judgemental Tags Ambiguous Meanings

Use of WordNet to categorise into groups based on synonyms. Use of music experts to merge groups by musical similarity

Generating Ground TruthSelecting Songs

Approaches: Tag identification Lyric counts Multi-label Classification

Mood Categories and Song Distributions

ExperimentsEvaluation Measures and Classifiers

Use of 10-fold Cross Validation Break data into 10 sets of size n/10. Train on 9 datasets and test on 1. Repeat 10 times and take a mean accuracy.

Classification with Support Vector Machines (SVM) Algorithms to analyse data and recognise patterns

ExperimentsLyric Preprocessing

Facts: Repetitions of words and sections: - Lack of verbatim transcripts Consisting of sections:

Intro, interlude, verse, etc. in the annotations Notes about song and instrumentation

Possible solution: Identifying and converting repetition and annotation patterns to actual repeated segments

ExperimentsLyrics Features

Common text classification tasks: Bag-of-words (BOW)

Collection of Unordered words Part-of-Speech (POS)

Use of Stanford Tagger Function Words (the, a, etc.)

Assigning of values: Frequency Tf-idf weight Normalised-frequency Boolean Value

ExperimentsStemming

Stemming – Merging words with same morphological roots

Snowball Stemmer Irregular nouns and verbs as inputs

Results

Text categorisation provides dimensionality and good generalisability POS Boolean representation is poorer because of high content of POS types in lyrics

Content words are more useful in mood classification

10th International Society for Music Information Retrieval Conference (ISMIR 2009)

Acknowledgement

Hu, X. et al. 2009. Lyric Text Mining in Music Mood Classification. International Music Information Retrieval Systems Evaluation Laboratory University of Illinois at Urbana-Champaign. [Online]. Pp.411-416. [Accessed 6 December 2013]. Available fromː http://ismir2009.ismir.net/proceedings/PS3-4.pdf

Training and Testing Data Sets. 2013. Training and Testing Data Sets. [Online]. [Accessed 5 December 2013]. Available from:http://technet.microsoft.com/en-us/library/bb895173.aspx.

Kohavi, Ron (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 2 (12): 1137–1143.(Morgan Kaufmann, San Mateo, CA)

D. Ellis, A. Berenzweig, and B. Whitman: The USPOP2002 Pop Music Data Set. Availablefromː http://labrosa.ee.columbia.edu/projects/musicsim/uspop2002.html.

Software & Additional Resources

http://www.music-ir.org/mirex/2007/index.php/AMChttp://en.wikipedia.org/wiki/MoodLogichttp://search.cpan.org/search%3fmodule=Lingua::Ident – Statistical language identifierhttp://snowball.tartarus.org/http://www.englishpage.com/irregularverbs/irregularverbs.htm - irregular verb listhttp://www.esldesk.com/eslquizzes/irregular-nouns/irregular-nouns.htm - irregular noun list http://nlp.stanford.edu/software/tagger.shtml - http://www.music-ir.org/mirex/2007/abs/AI_CC_GC_MC_AS_tzanetakis.pdf - POS Taggerhttp://www.music-ir.org/archive/figs/18moodcat.htm - Mood Categories & Song Distributionshttp://www.originlab.com/index.aspx?go=Products/Origin/Statistics/NonparametricTests&pid=1087 – Performance identifier