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Annotating Music and Lyrics

Annotating Music and Lyrics. Kristine Monteith CS 652 - Research Project June 11, 2009. Project Goal. Find a suitable song for a given situation Applications Indexing songs by topic and mood Soundtracks for movie scenes Music therapy groups. What do we want to label?. Lyrics Themes

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Annotating Music and Lyrics

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  1. Annotating Music and Lyrics Kristine Monteith CS 652 - Research Project June 11, 2009

  2. Project Goal • Find a suitable song for a given situation • Applications • Indexing songs by topic and mood • Soundtracks for movie scenes • Music therapy groups

  3. What do we want to label? • Lyrics • Themes • Moods • Music • Moods • Energy • Emotional evocativeness • Genre

  4. Song Ontology

  5. How to Extract Features:Lyrics • Current state: Keyword search • Find all occurrences of the search term in the song • Find all occurrences of search term synonyms (Using WordNetsynsets) • Future work: • Extend searches to phrases • Determining mood

  6. Supervised Learning Prediction of target label Labeled Training Data Classifier Features of a Musical Selection

  7. Input Features • Bag of Words • Words appearing on page and word counts  Looking for other methods to analyze documents and collect input features

  8. Output Labels • Derived from questionnaire • Hand-labeled by researchers

  9. How to Extract Features:Music • Target labels to predict • Moods (labeled by subject or expert) • Energy (determined by direction of change in biofeedback responses) • Emotional evocativeness (determined by extent of change in biofeedback responses) • Genre (labeled by subject, expert, or clustering)

  10. Input Features:Acoustic Properties • Spectral Centroid • Spectral Rolloff Point • Spectral Flux • Compactness • Spectral Variability • Root Mean Square • Fraction of Low Energy Windows • Zero Crossings • Strongest Beat • Beat Sum • Strength of Strongest Beat • Strongest Frequency Via Spectral Centroid • Strongest Frequency Via FFT Maximum

  11. Input Features:Acoustic Properties • MFCC • LPC • Method of Moments • Partial Based Spectral Centroid • Partial Based Spectral Flux • Peak Based Spectral Smoothness • Relative Difference Functions • Area Method of Moments

  12. Input Features:Symbolic Features • Tempo • Key • Mode • Musical form • Rhythmic structure • Vocalization • Instrumentation • Melodic contour • Harmonic patterns

  13. Output Labels:Questionnaire-based Responses

  14. Output Labels:Physiological Responses • Heart rate • Breathing rate • Perspiration • Skin temperature • Each subject will listen to one minute segments separated by one minute of silence

  15. Conclusion • Demo: Music Therapist Assistant • Any Questions/Suggestions?

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