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Voice Separation A Local Optimization Approach Jurgen Kilian Holger H. Hoos

Voice Separation A Local Optimization Approach Jurgen Kilian Holger H. Hoos. Xiaodan Wu Feb. 26 2003. Introduction. What is Voice Separation? (score) Some of the Usages To obtain usable scores from performances of polyphonic music

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Voice Separation A Local Optimization Approach Jurgen Kilian Holger H. Hoos

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  1. Voice SeparationA Local Optimization ApproachJurgen KilianHolger H. Hoos Xiaodan Wu Feb. 26 2003

  2. Introduction • What is Voice Separation? (score) • Some of the Usages • To obtain usable scores from performances of polyphonic music • To improve the music retrieval systems that only support monophonic music

  3. Existing Approaches • Split Point Separation (figure) • Rule Based Approaches • Prefer small intervals between succeeding notes • Keep range of a voice small • Use a small number of voices • Avoid crossings of voices

  4. Overview The method developed by the authors • Goal: To create reasonable and flexible score-notation for various needs • Technique and Algorithm • Preprocessing • The Cost Function • Stochastic Local Search Approach

  5. Preprocessing • Removes small overlaps, quantizes the notes • Partitions the musical piece into slices.

  6. The Cost Function • Pitch Distance Penalty Penalize large pitch intervals between successive notes in a voice. • Gap Distance Penalty Penalize large gaps/rests between successive notes in a voice. • Chord Distance Penalty Penalize chords with a large pitch interval between the highest and the lowest note in a voice, as well as irregular chords. • Overlap Distance Penalty Penalize overlaps between successive notes in the same voice.

  7. Cost-Optimized Slice Separation • Using a stochastic local search approach • A fixed number of steps was employed to terminate the improvement

  8. Implementation and Results • Implemented in midi2gmn, a program for converting MIDI into GUIDO Music Notation. • The code was written in ANSI C++ . • All the penalty parameters and the maximum number of voices could be set in an initialization file. • With the correct parameter settings, the tested Bach chorals and inventions were separated almost entirelycorrectly. • Drawback: If a voice continues with a large interval step after a rest, the algorithm acts incorrectly.

  9. Comments on the future work • Cost Functions could be improved by employing more rules in music theory. • The thought of Expectancy could be combined with the cost functions.

  10. score

  11. Split point separation

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