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An Intelligent Hybrid Model for Chord Prediction

An Intelligent Hybrid Model for Chord Prediction. Sidney Cunha & Geber Ramalho {usgcc,glr}@di.ufpe.br Departamento de Informática - UFPE. The task: real time chord prediction in music accompaniment. How musicians can play “on the flight” an unknown song?

marvin-albert
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An Intelligent Hybrid Model for Chord Prediction

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  1. An Intelligent Hybrid Model for Chord Prediction Sidney Cunha & Geber Ramalho {usgcc,glr}@di.ufpe.br Departamento de Informática - UFPE

  2. The task: real time chord prediction in music accompaniment • How musicians can play “on the flight” an unknown song? • Given the chord sequence (and a melody) played so far, which is the next chord?

  3. Why predict musical chords? • Automatic accompaniment systems • band-in-a-box, jammer, ImPact, ... • embedded sequencers • Important class of problems: Time Series Prediction • financial applications • powers systems applications • sales evolution

  4. Difficulties • Music theory does not provide any precise rules for chord prediction • Chord prediction depends on many variables • the music style • the musicians themselves • the very song being played, ...

  5. Difficulties • It is not simple to formalize music knowledge • How to represent chords and chord sequences? • How to extract on-line structural information from songs? • How to represent and use prior knowledge (II-V-I, I-VI-IIm-I)? • Chord prediction is a hard real time task

  6. State of the art • Various works on predicting musical parameters • For chord prediction: Carnegie-Mellon (Thom & Dannemberg 95) • Prediction of Jazz songs chords • n-gram probabilistic models algorithm • 3 learning methods • Off-line: (a) training on 30 songs (b) using model • On-line: no prior knowledge, only on-line adaptation • Hybrid: prior knowledge + on-line adaptation

  7. Results • Main claims • better results were achieved combining prior with on-line knowledge • Limitations • The corpus’ songs were in C tonality • Melody was not considered • Poor chord representation (root, category, 4-note chords, no duration) • Error rate: 42%

  8. Our approach on chord prediction • Musical corpus • Jazz: complexity and richness • 60 songs in their original tonality • Prediction technique • neural networks (=> prior knowledge on typical chord sequences) • Online adaptation • knowledge based (=> sequence tracking rules)

  9. Early work on the predictor • Rich chord characterization • Root: C, D, E, A, Bb, G#, ... • Category: min, maj7, 7, m7(b5), ... • Duration: 1 beat, 1 measure, ... • interval w.r.t. the next or previous chord root • other: position within the song, position within the measure, ... • time window: 3 previous chords • Test different learning algorithms • Neural networks (MLP-backpropagation, MLP-rbf, …), ID3, ...

  10. Neural net predictor • MLP-backpropagation neural network

  11. Neural net predictor • MLP-backpropagation neural network

  12. Early predictor results • However, it could be better taking into account • musical structure (recurrent chord sequences) • refrains, chorus, sessions (AABA, ABAB,...), etc. • Melody • Basin Street Blues

  13. Two possible solutions • On-line learning on the (neural net) predictor • technically rejected • Capture musicians knowledge on on-line chord prediction • Sequence tracker!! • Goal: identify, with absolute certainty, recurrent sequences

  14. Sequence tracker • Rules • One can only guarantee that a chord sequence is starting to be repeated after the co-incidence of at least three consecutive measures (including melody); • In many cases, repeated sequences inside of a song are not completely repeated, presenting some differences in the measures 8n or 8n+1, where n=1,2,3....; • Two identical consecutive chords or measures cannot be tested as belonging to different sequences because this can generate a loop; • the whole song is a repeatable sequence; • etc.

  15. Combining the neural net predictor with the sequence tracker Hybrid System Neural Net next chord Sequence Tracker Chords

  16. Results Best results error rate (%) • Depending on the song, error rate decreases as the song is being played! • 3%/repetition in average MLP-backpropagation (without ST): 11,2% MLP-backpropagation (with ST 1 repetition): 9%

  17. Final remarks • Results • original hybrid model for chord prediction • pretty better results than those of previous works • Future works • Extend model to chord prediction in other styles • Adapt model to generate new harmonies • Adapt model to recognize music styles • Introduce on line feedback on wrong chords • Combine with ImPact

  18. Sound Example: So Nice 1st execution 7 erros (in red): 4, 5, 13, 16, 25, 27, 30

  19. Sound Example: So Nice 2nd execution 4 erros (in red): 5, 16, 27, 30

  20. Sound Example: So Nice 3rd execution 0 erros!

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