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Automatic Pitch Spelling

Presentation. Automatic Pitch Spelling. From Numbers to Sharps and Flats. Emilios Cambouropoulos. Xiaodan Wu Feb.12 2003. About the author. Emilios Cambouropoulos completed his PhD thesis on Music and Artificial Intelligence at the University of Edinburgh.

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Automatic Pitch Spelling

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  1. Presentation Automatic Pitch Spelling From Numbers to Sharps and Flats Emilios Cambouropoulos Xiaodan Wu Feb.12 2003

  2. About the author Emilios Cambouropoulos completed his PhD thesis on Music and Artificial Intelligence at the University of Edinburgh. Till February 1999, he worked as a research associate at King's College London on a musical data-retrieval project: Musical Similarity and Melodic Recognition. Currently, he is working as a research fellow at the Austrian Research Institute for Artificial Intelligence on the project: Artificial Intelligence Models of Musical Expression. • Publication we are going to study: • The Local Boundary Detection Model (LBDM) and its Application in the Study of Expressive Timing • From MIDI to Traditional Musical Notation

  3. What’s the challenge? • An example from the pitch representation in MIDI and its alternative spelling • It’s polyphonic • No prior knowledge such as • Key signature • Tonal centers • Time signature • Voice separation 60 Midi Pitch D | C Alternative spelling | B# bb

  4. The previous works

  5. The pitch spelling algorithm • The input to the algorithm is a list of MIDI pitch values • The optimization procedure relies on two fundamental principles: • Notational Parsimony • Interval Optimization • Penalty values are introduced.

  6. The pitch spelling algorithmcontinue

  7. The pitch spelling algorithmcontinue Penalty Values: • Notational Parsimony ‘normal’ spelling of note 0 enharmonic spelling of note 4 • Interval Optimization Class A or B 0 Class C 1 ClassD 3 For each spelled pitch sequence, all the penalty values for every possible intervals are summed. And the sequence with the lowest penalty value is selected.

  8. The pitch spelling algorithmcontinue • A shifting overlapping windowing technique is hired to pick up certain sequence for spelling pitches • The length of the window could be modified.

  9. The Result Experiment 1 Experiment 2 Experiment 3

  10. The drawback • There is a trade-off for the different pitch interval orderings. • The technique to select the length for the window. • Voice-leading concerns are not currently considered.

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