Which Line Is it Anyway : Clustering for Staff Line Removal in Optical Music Recognition. Sam Vinitsky
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1 Which Line Is it Anyway : Clustering for Staff Line Removal in Optical Music Recognition Sam Vinitsky
2 Optical Music Recognition: An Overview
3 Optical Music Recognition: An Overview
4 Sheet Music Primer
5 Sheet Music Primer: Staff Lines
6 Sheet Music Primer: Symbols
7 Back to Optical Music Recognition...
8 Applications of OMR: Handwritten Music
9 Optical Music Recognition: Pipeline
10 Optical Music Recognition: Pipeline 1. Find and remove staff lines
11 Optical Music Recognition: Pipeline Find and remove staff lines Find symbols
12 Optical Music Recognition: Pipeline = note 2 = treble clef 3 = sharp 4 = sharp 5 = three 6 = note... Find and remove staff lines Find symbols Determine type of each symbol
13 Optical Music Recognition: Pipeline = note 2 = treble clef 3 = sharp 4 = sharp 5 = three 6 = note... (G4) (first clef) (key sig with 4) (key sig with 3) (triplet for ) (E5, triplet 5) Find and remove staff lines Find symbols Determine type of each symbol Determine how symbols relate to each other
14 Optical Music Recognition: Pipeline = note 2 = treble clef 3 = sharp 4 = sharp 5 = three 6 = note... (G4) (first clef) (key sig with 4) (key sig with 3) (triplet for ) (E5, triplet 5) Find and remove staff lines Find symbols Determine type of each symbol Determine how symbols relate to each other Convert into computer readable format (musicxml, MIDI, etc...)
15 Optical Music Recognition: Pipeline = note 2 = treble clef 3 = sharp 4 = sharp 5 = three 6 = note... (G4) (first clef) (key sig with 4) (key sig with 3) (triplet for ) (E5, triplet 5) *** Find and remove staff lines *** Find symbols Determine type of each symbol Determine how symbols relate to each other Convert into computer readable format (musicxml, MIDI, etc...)
16 Staff Line Removal: Overview Input: original score image Output: staffless image
17 Reminder: Hough Transform...
18 Staff Line Removal...
19 Staff Line Removal...
20 Staff Line Removal...
21 Staff Line Removal...
22 Staff Line Removal: Algorithms Handcrafted: - Run-length Shortest Path Etc [Carter & Bacon 92] [Cardoso et al. 08] [see Dalitz et al. 08]
23 Staff Line Removal: Algorithms Handcrafted: - Run-length Shortest Path Etc [Carter & Bacon 92] [Cardoso et al. 08] [see Dalitz et al. 08] Supervised Machine Learning: (State of the Art) - Classification of pixels Deep learning on image - Convolutional Neural Networks - Generative Adversarial Networks [Calvo-Zarogoza et al. 16] [Calvo-Zarogoza et al. 17] [Bhunia et al. 18]
24 Staff Line Removal: Algorithms Handcrafted: - Run-length Shortest Path Etc [Carter & Bacon 92] [Cardoso et al. 08] [see Dalitz et al. 08] Supervised Machine Learning: (State of the Art) - Classification of pixels Deep learning on image - Convolutional Neural Networks - Generative Adversarial Networks [Calvo-Zarogoza et al. 16] [Calvo-Zarogoza et al. 17] [Bhunia et al. 18] Unsupervised Machine Learning: - Clustering of pixels [Vinitsky 18]
25 Staff Line Removal via Pixel Clustering Algorithm: (Vinitsky 18) 1. Convert each black pixel into a feature vector Image modified from
26 Staff Line Removal via Pixel Clustering Image source: Calvo-Zaragova et al., 16
27 Staff Line Removal via Pixel Clustering Algorithm: (Vinitsky 18) 1. Convert each black pixel into a feature vector Image modified from
28 Staff Line Removal via Pixel Clustering Algorithm: (Vinitsky 18) Convert each black pixel into a feature vector Cluster the feature vectors into two groups Image modified from
29 Staff Line Removal via Pixel Clustering Algorithm: (Vinitsky 18) Convert each black pixel into a feature vector Cluster the feature vectors into two groups Figure out which cluster is staff and which is non-staff Image modified from
30 Staff Line Removal: Results * = implemented ** = original algorithm
31 Staff Line Removal: Results Clustering output Ground truth staff-less
32 Staff Line Removal: Results Clustering output Ground truth staff-less
33 Future Work Clustering for Staff Line Removal: - Other clustering methods
34 Future Work Clustering for Staff Line Removal: - Other clustering methods Better feature vectors
35 Future Work Clustering for Staff Line Removal: - Other clustering methods Better feature vectors Picking the staff cluster
36 Future Work Clustering for Staff Line Removal: - Other clustering methods Better feature vectors Picking the staff cluster White pixels can be staff (due to noise)
37 Future Work Clustering for Staff Line Removal: - Other clustering methods Better feature vectors Picking the staff cluster White pixels can be staff (due to noise) Optical Music Recognition: - The rest of the pipeline...
38 References [1] Jorge Calvo-Zaragoza, Luisa Mico, and Jose Oncina. Music staff removal with supervised pixel classification. International Journal on Document Analysis and Recognition (IJDAR), 19(3): , Sep [2] Jorge Calvo-Zaragoza, Jose J. Valero-Mas, and Antonio Pertusa. End-to-end optical music recognition using neural networks. In ISMIR, [3] Jaime Cardoso, Artur Capela, Ana Rebelo, and Carlos Guedes. A connected path approach for staff detection on a music score, [4] I. Fujinaga, M. Droettboom, C. Dalitz, and B. Pranzas. A comparative study of staff removal algorithms. IEEE Transactions on Pattern Analysis & Machine Intelligence, 30: , [5] A. Konwer, A. K. Bhunia, A. Bhowmick, P. Banerjee, P. Pratim Roy, and U. Pal. Staff line Removal using Generative Adversarial Networks. ArXiv e-prints, [6] J. Novotny and J. Pokorny. Introduction to optical music recognition: Overview and practical challenges [7] N.P. Carter, R.A. Bacon: Automatic Recognition of Printed Music. In H.S. Baird, H. Bunke, K. Yamamoto (editors): Structured Document Image Analysis, pp , Springer, 1992.
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