inferring the meaning of chord sequences via lyrics tom o'hara computer science department...
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Inferring the meaning of chord sequences via lyrics
Tom O'HaraComputer Science DepartmentTexas State UniversitySan Marcos, [email protected]
WOMRAD-11: 2nd Workshop on Music Recommendation and Discovery
23 October 2011
Talk OverviewIntroduction: Lyric chord annotations for
unsupervised learningBackground: Supervised music categorization;
parallel corporaProcess: Cooccurrence statistics via contingency
tablesAnalysis: Major vs. minor chord associationsConclusion: Summary and future plans
Introduction OverviewTypical music recommendation approachParallel text corporaMore resources for music recommendationOnline tabs and chords
IntroductionTypical music recommendation approach
o Suggest songs based on common categories (e.g., mood)
o Human annotations of song categoryo Typically done at song levelo Tedious/subjective to do at segment level
Parallel text corporao Same documents in two or more languageso Developed for human consumption (e.g., UN)o Invaluable for automatic machine translation
Introduction (continued)New resource for music recommendation
o Guitar tablature (tabs)o Kept up to date by musicianso Augments human annotationso Finer granularity (chord sequence vs. song)
Online tabs and chordso Usenet (e.g., alt.guitar.tabs, 10K+)oWeb sites (e.g., Chordie, 200K+)
Background OverviewLearning meaning of musicTranslation lexicon induction
BackgroundLearning meaning of music
o Supervised classification• User annotations: Turnbull et al. (2008)
o Unsupervised classification• Online reviews: Whitman and Ellis (2004)
o Lyric analysis and social tags:• Affect filtering: Hu at al (2009)• Usage, readability, etc.: McKay el al. (2010)
Translation lexicon inductiono Co-occurrence analysis: Fung and Church (1994)o Linkage refinements: Melamed (2000)
Process Steps1. Obtain song data with chords annotated2. Extract lyrics proper (with annotations)3. Optional: Map lyrics into meaning categories
o a. Get tagged data on meaning categories for lyricso b. Preprocess lyrics and untagged chord
annotationso c. Train to categorize over words and hypernymso d. Classify each lyric line from chord annotations
4. Fill contingency table5. Determine chord(s)/token associations
Obtain song data with chords annotated
Taken from Usenet alt.guitar.tabs forumo CRD in subject line
Sample[C] They're gonna put me in the [F] movies[C] They're gonna make a big star out of [G] meWe'll [C] make a film about a man that's sad and [F] lonelyAnd [G7] all I have to do is act [C] naturally
Lyrics are from "Act Naturally" by Johnny Russell and Voni Morrison, with chord annotations for song as recorded by Buck Owens.
Extract lyrics proper (with annotations)o Removes e-mail headers and other extraneous texto Two column table (one row per chord change)• Chord; and words for that chords• Includes end of line and verse indicators
SampleC They're gonna put me in theF movies <endl>C They're gonna make a big star out ofG me <endl> We'llC make a film about a man that's sad andF lonely <endl> AndG7 all I have to do is actC naturally <endl> <endp>
Mapping lyrics via supervised classification
Get tagged data on meaning categories for lyricsPreprocess lyrics and untagged chord annotationsTrain to categorize over words and hypernymsClassify each lyric line from chord annotations
Get tagged data on meaning categories for lyrics
CAL500 for training data Turnbull et al. (2008)
o 500 songs (but only 300 lyrics obtained)o Annotated by 3 userso 135 categories in broad groups
Emotion Category FrequencyLabel freq Label freqAngry-Aggressive 31 Laid-back-Mellow 7Arousing-Awakening 77 Light-Playful 1Bizarre-Weird 7 Loving-Romantic 1Calming-Soothing 91 Pleasant-Comfortable 3Carefree-Lighthearted 28 Positive-Optimistic 0Cheerful-Festive 9 Powerful-Strong 3Emotional-Passionate 23 Sad 3Exciting-Thrilling 2 Tender-Soft 2Happy 6
Preprocess lyrics and chord annotations
Isolate punctuationAdd semantic classes for each word
oWordNet hypernyms Miller (1990)
WordNet Samplemovie#1, film#1, picture#6, moving picture#1, ... => show#3 => social event#1 => event#1 => ... => product#2, production#3 => creation#2 => artifact#1, artefact#1 => whole#2, unit#6 => ...
Train to categorize w/ words & hypernyms
Rainbow text categorization McCallum (1996)
o Song documents with meaning category labelso Tokens for Words and WordNet semantic classeso Default Rainbow settings (e.g., no stemming)o TF/IDF feature selection
Other WordNet text categorization workoMansuy and Hilderman (2006)
Classify each lyric line from annotations
Each line classified as mini-documento Verse included for more context
Original annotationsC They're gonna put me in theF movies <endl>...G7 all I have to do is actC naturally <endl> <endp>
ResultC Light-PlayfulF Light-Playful...G7 Light-PlayfulC Light-Playful
Back to Main Process1. Obtain song data with chords annotated2. Extract lyrics proper (with annotations)3. Optional: Map lyrics into meaning categories=>4. Fill contingency table5. Determine chord(s)/token associations
Fill contingency tableGeneral Format
X \ Y + -+ X Y X ¬Y- ¬X Y ¬X ¬Y
SampleG versus 'film' + -+ 1 2,213- 0 17,522
Determine chord(s)/token associations
Compute co-occurrence statisticsEx: Average Mutual Information
x y y) = (Y P x)= (X P
y) = Y x,= (X Plog y) = Y x,= (X P 2
Analysis OverviewIndividual chords with word tokensChord sequences with meaning category tokens
Individual chords with word tokensMajor vs. minor key differences
Avg. MI Chord Word XY X¬Y ¬XY.00034 C happy 7 1,923 13.00005 G happy 4 2,210 16.00030 Dm happy 3 341 17.00008 Em happy 2 548 18.00176 F bright 10 971 3.00018 Am bright 3 962 10.00071 Bm sad 3 197 4.00032 Bb sad 2 325 5.00039 Em sad 3 1,097 6.00542 Dm sorrow 2 342 5.00068 C sorrow 2 1,928 5
Chord sequences with meaning category tokens
Most frequent chord sequence associationsAvg. MI Chord Sequence Category XY X¬Y ¬XY.0027 D7, D7, D7, D7 Bizarre 30 36 1,358.0037 Em, G, G6, Em Carefree 18 6 594.0032 D, A, A, C#min Carefree 14 2 598.0032 C#min, D, A, A Carefree 14 2 598.0032 A, C#min, D, A Carefree 14 2 598.0032 A, A, C#min, D Carefree 14 2 598.0012 D7, G, C, G Bizarre 14 17 1,374.0018 C, D7, G, C Bizarre 14 19 1,374.0022 D, A, A, D Powerful 13 8 667.0014 C, D, C, D Happy 13 39 502
Conclusion OverviewSummaryFuture workReferences
SummaryIntroduction: Lyric chord annotations for
unsupervised learningBackground: Supervised music categorization;
parallel corporaProcess: Cooccurrence statistics via contingency
tablesAnalysis: Major vs. minor associations; sequence
samples
ConclusionCan learn meaning of chord sequences from
annotated lyricsLarge untapped resource now exploitable for
music recommendation
Future workObjective measures for evaluation
o Complication: subjectivity of chord sequence meaning
Additional aspects of music for modeling meaningo ex: Tempo and note sequences
ReferencesP. Fung and K. W. Church. K-vec: A new approach for aligning parallel texts. In Proc.
COLING, 1994.X. Hu, J. S. Downie, and A. F. Ehman. Lyric text mining in music mood classification. In
Proc. ISMIR, pages 411-6, 2009.T. Mansuy and R. Hilderman. Evaluating WordNet features in text classification models.
In Proc. FLAIRS, 2006.A. K. McCallum. Bow: A toolkit for statistical language modeling, text retrieval,
classification and clustering. www.cs.cmu.edu/ mccallum/bow, 1996.∼C. McKay et al. Evaluating the genre classification performance of lyrical features
relative to audio, symbolic and cultural features. In Proc. ISMIR, 2010.I. D. Melamed. Models of translational equivalence among words. Computational
Linguistics, 26(2):221-49, 2000.G. Miller. Special issue on WordNet. International Journal of Lexicography, 3(4), 1990.C. Schmidt-Jones and R. Jones, editors. Understanding Basic Music Theory. Connexions,
2007. http://cnx.org/content/col10363/latest.D. Turnbull et al. Semantic annotation and retrieval of music and sound effects. IEEE
TASLP, 16 (2), 2008.B. Whitman and D. Ellis. Automatic record reviews. In Proc. ISMIR, 2004.