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Discotective

This project presents an Optical Music Recognition (OMR) system designed to assist music students in learning to read sheet music effectively. By capturing images of sheet music, the system analyzes musical symbols and converts them into audio signals. The solution aims to ease the learning process by providing audio feedback, enhancing digital archiving, and allowing for electronic distribution and editing of music. Although current software lacks integrated functionality, our system offers innovative preprocessing, segmentation, and classification techniques to recognize and synthesize music accurately.

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Discotective

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Presentation Transcript

  1. Discotective Final presentation 19 april 2011 Katie Bouman • Brad Campbell • Mike Hand • Tyler Johnson • Joe Kurleto

  2. Project Overview • Optical Music Recognition (OMR) system • Takes in image of sheet music • Finds and analyzes musical symbols • Outputs captured song as audio signal

  3. Motivation • Music students • Learning to read sheet music is difficult • Knowing how the music should sound makes it easier • Digital archival • Longevity • Electronic availability & distribution • Possibility for editing

  4. Music Recognition Systems • Current software • SmartScore (Musitek) • SharpEye (Musicwave) • Notescan – Nightingale • SightReader – Finale • Photoscore – Sibelius (Neuratron) • None are embedded • Use scanners for image acquisition

  5. The Design • Preprocessing • Segmentation • Classification • Audio synthesis

  6. The Design • Preprocessing • Segmentation • Classification • Audio synthesis

  7. Preprocessing Adaptive binarization Original image Adaptive threshold Binarized image

  8. Preprocessing Skew correction

  9. Preprocessing Cropping Original image Cropped image

  10. The Design • Preprocessing • Segmentation • Classification • Audio synthesis

  11. Segmentation • Staff detection Original image Y-projection

  12. Segmentation • Line removal Original image Stafflines removed

  13. Segmentation • Stem & measure marker detection Original image X-projection

  14. Segmentation • Remove stemmed notes from image • Find locations of remaining symbols Original image Notes removed, symbols located

  15. The Design • Preprocessing • Segmentation • Classification • Audio synthesis

  16. Classification • Stemmed notes • Pitch • Duration • Note-head type • Eighth-note tail

  17. Classification • Remaining symbols • Whole notes • Rests • Accidentals • Dots • Classified via extracted features • Symbol dimensions • Proximity to other symbols • Presence of vertical lines • Black-to-white pixel ratio accidental classification(based on vertical lines)

  18. The Design • Preprocessing • Segmentation • Classification • Audio synthesis

  19. Audio Synthesis Direct digital synthesis Multiple FTV values for harmonics Amplitude Amplitude Frequency (Hz) Samples FFT Time signal

  20. Hardware Implementation • Hardware • Altera DE2 FPGA with Nios II softcore processor • Altera D5M 5 Megapixel Camera • Hardware limitations • 50 MHz clock • Memory space for only ~1.5 grayscale image copies • Lens distortion • Streamlined algorithms

  21. Capabilities • Supported • Notes/rests up to eighth-beat speed • All key signatures • Accidentals • Dotted notes • Unsupported • Skew correction (in hardware) • Adaptive binarization (in hardware) • Chords • Ties/Slurs • Multiple melodies/harmonies • Repeat markers, DC al Coda, etc

  22. EMO

  23. Thank you for your time. Can we entertain any questions?

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