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Automatic Lyric Suggestion System for Music Composition and Recommendation

This project presents an innovative automatic lyric suggestion system for music enthusiasts and composers. By utilizing advanced techniques such as evolutionary algorithms and constrained Markov models, the system aims to generate meaningful, grammatically correct, and poetic lyrics that adhere to specific constraints like rhythm and rhyme. Potential applications include generating summaries of lyrics for music recommendation services and creating constrained translations that maintain the original's poetic structure. The system leverages a phonemic dictionary and a styled corpus to enhance the lyric generation process.

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Automatic Lyric Suggestion System for Music Composition and Recommendation

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  1. An Automatic Lyric Suggestion System 7 April 2013 For MUMT 621 Nicholas Esterer

  2. Why? • Providing a convenient tool for music enthusiasts and composers to easily add lyrics to pieces of music. • Eventual uses may be (Barbieri 2012): • Generating summaries of musical lyrics for use in music recommendation systems. • Creating constrained translations of lyrics that fit the metre, rhyme and rhythmic stresses of the original language.

  3. How? • For poetry, Manurung (2003) proposes the use of evolutionary algorithms (Mitchell 1996) for multiple constraint search, the constraints being: “meaningfulness”, “grammaticality” and “poeticness”. • Poetry writing can then be seen as a multiobjective optimization problem, where formal and semantic solutions are to be maximized.

  4. Barbieri et al. (2012) use a technique of “constrained Markov models” which use templates for: • form (when verses repeat) • rhythm and stress (what syllables are stressed) • rhyme (what verses rhyme and where) • parts-of-speech • These constrain a random Markov process that generates lyrics according to conditional character or word probabilities. • Semantic relationships can be constrained by use of a technique described in Milne and Witten (2008) (the Wikipedia links distance).

  5. The Proposed Technique • Based on the technique of Barbieri et al. (2012), the steps are: • Determine the pronunciation of some corpus of sentences using the CMU Pronouncing Dictionary (2008). • Determine the phonemic stress pattern of each sentence using the above dictionary. • A rhythmical-stress and rhyme-form query will be submitted to a database storing corpus characteristics and a solution will be suggested.

  6. Diagram of the Technique Text in bold means the words should rhyme in the result. Sentence from corpus: The quick brown fox jumps. Pronunciation: DH AH0 K W IH1 K B R AW1 N F AA1 K S JH AH1 M P S Phonemic stress template: 0 1 1 1 1 Rhyming sentence pronunciation: M AH0 K B EH1 TH K OW1 CH L AA1 B K L AH1 M P S Resulting sentence: Macbeth coach lob clumps.

  7. Improvements • Use a part-of-speech tagger from the Natural Language Processing Toolkit (Bird et al. 2009) to give grammatically correct results. • Use a corpus of lyrics aligned to pitch and rhythm to give “most probable” settings of text to music and vice versa.

  8. Bibliography • Found here: • http://www.music.mcgill.ca/~nester/final_project_bibliography_full.html

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