Optical Music Recognition Method Combining Multi-Scale Residual Convolutional Neural Network and Bi-Directional Simple Recurrent Units

Qiong Wu; Qiang Li; Xin Guan

doi:10.3788/LOP57.081006

[1] Bainbridge D, Bell T. The challenge of optical music recognition[J]. Computers and the Humanities, 35, 95-121(2001).

[2] Calvo-Zaragoza J, Vigliensoni G, Fujinaga I. Pixel-wise binarization of musical documents with convolutional neural networks. [C]∥2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), May 8-12, 2017, Nagoya, Japan. New York: IEEE, 362-365(2017).

[3] Timofte R, van Gool L. Automatic stave discovery for musical facsimiles[M]. ∥Lee K M, Matsushita Y, Rehg J M, et al. Computer vision-ACCV 2012. Lecture notes in computer science. Berlin, Heidelberg: Springer, 7727, 510-523(2013).

[4] Gallego A J, Calvo-Zaragoza J. Staff-line removal with selectional auto-encoders[J]. Expert Systems with Applications, 89, 138-148(2017).

[5] Visaniy M, Kieu V C, Fornes A et al. ICDAR 2013 music scores competition: staff removal. [C]∥2013 12th International Conference on Document Analysis and Recognition, August 25-28, 2013, Washington, DC, USA. New York: IEEE, 1407-1411(2013).

[6] Pacha A, Eidenberger H. Towards a universal music symbol classifier. [C]∥2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), November 9-15, 2017, Kyoto, Japan. New York: IEEE, 35-36(2017).

[7] Rebelo A, Capela G, Cardoso J S. Optical recognition of music symbols[J]. International Journal on Document Analysis and Recognition, 13, 19-31(2010).

[8] Vo Q N, Kim S H, Yang H J et al. An MRF model for binarization of music scores with complex background[J]. Pattern Recognition Letters, 69, 88-95(2016).

[9] dos Santos Cardoso J, Capela A, Rebelo A et al. Staff detection with stable paths[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 1134-1139(2009).

[10] Wu T L, Li Q, Guan X. Lightweight staff removal method based on multidimensional local binary pattern and XGBoost[J]. Laser & Optoelectronics Progress, 56, 061006(2019).

[11] Calvo-Zaragoza J, Gallego A J, Pertusa A. Recognition of handwritten music symbols with convolutional neural codes. [C]∥2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), November 9-15, 2017, Kyoto, Japan. New York: IEEE, 691-696(2017).

[12] Wang J, Wu X S. Medical image fusion based on improved guided filtering and dual-channel pulse coupled neural networks[J]. Laser & Optoelectronics Progress, 56, 151004(2019).

[13] Ma Y J, Ma Y T, Chen J H. Vehicle recognition based on multi-layer features of convolutional neural network and support vector machine[J]. Laser & Optoelectronics Progress, 56, 141001(2019).

[14] Haji J Jr, bounding box regression[EB/OL]. -08-05)[2019-06-25]. https:∥arxiv.xilesou., top/abs/1708, 01806(2017).

[15] Choi K Y, Couasnon B, Ricquebourg Y et al. Bootstrapping samples of accidentals in dense piano scores for CNN-based detection. [C]∥2017 14th IAPR International Conference on Document Analysis and Recognition, November 9-15, 2017, Kyoto, Japan. New York: IEEE, 19-20(2017).

[16] Calvo-Zaragoza J. Valero-Mas J J, Pertusa A. End-to-end optical music recognition using neural networks. [C]∥Proceedings of the 18th International Society for Music Information Retrieval Conference, October 23-27, 2017, Suzhou, China. [S.l.: s.n.], 23-27(2017).

[17] Tuggener L, Elezi I, Schmidhuber J et al. Deep watershed detector for music object recognition. [C]∥Proceedings of the 19th International Society for Music Information Retrieval Conference, September 23-27, 2018, Paris, France. [S.l.: s.n.], 271-278(2018).

[18] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[20] Lei T, Zhang Y, Wang S I et al. Simple recurrent units for highly parallelizable recurrence. [C]∥Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, October 31-November 4, 2018, Brussels, Belgium. Brussels: Association for Computational Linguistics, 4470-4481(2018).

[21] Graves A, Fernández S, Gomez F et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. [C]∥Proceedings of the 23rd International Conference on Machine Learning '06, June 25-29, 2006, Pittsburgh, Pennsylvania, USA. New York: ACM, 369-376(2006).