An efficient musical accompaniment parallel system for mobile devices

[EN] This work presents a software system designed to track the reproduction of a musical piece with the aim to match the score position into its symbolic representation on a digital sheet. Into this system, known as automated musical accompaniment system, the process of score alignment can be carried out real-time. A real-time score alignment, also known as score following, poses an important challenge due to the large amount of computation needed to process each digital frame and the very small time slot to process it. Moreover, the challenge is even greater since we are interested on handheld devices, i.e. devices characterized by both low power consumption and mobility. The results presented here show that it is possible to exploit efficiently several cores of an ARM(A (R)) processor, or a GPU accelerator (presented in some SoCs from NVIDIA) reducing the processing time per frame under 10 ms in most of the cases.This work was supported by the Ministry of Economy and Competitiveness from Spain (FEDER) under projects TEC2015-67387-C4-1-R, TEC2015-67387-C4-2-R and TEC2015-67387-C4-3-R, the Andalusian Business, Science and Innovation Council under project P2010-TIC-6762 (FEDER), and the Generalitat Valenciana PROMETEOII/2014/003Alonso-Jordá, P.; Vera-Candeas, P.; Cortina, R.; Ranilla, J. (2017). An efficient musical accompaniment parallel system for mobile devices. The Journal of Supercomputing. 73(1):343-353. https://doi.org/10.1007/s11227-016-1865-xS343353731Cont A, Schwarz D, Schnell N, Raphael C (2007) Evaluation of real- time audio-to-score alignment. In: Proc. of the International Conference on Music Information Retrieval (ISMIR) 2007, ViennaArzt A (2008) Score following with dynamic time warping. An automatic page-turner. Master’s Thesis, Vienna University of Technology, ViennaRaphael C (2010) Music plus one and machine learning. In: Proc. of the 27 th International Conference on Machine Learning, Haifa, pp 21–28Carabias-Ortí JJ, Rodríguez-Serrano FJ, Vera-Candeas P, Ruiz-Reyes N, Cañadas-Quesada FJ (2015) An audio to score alignment framework using spectral factorization and dynamic time warping. In: Proc. of the International Conference on Music Information Retrieval (ISMIR), Málaga, pp 742–748Cont A (2010) A coupled duration-focused architecture for real-time music-to-score alignment. IEEE Trans. Pattern Anal. Mach. Intell. 32(6):974–987Montecchio N, Orio N (2009) A discrete filterbank approach to audio to score matching for score following. In: Proc. of the International Conference on Music Information Retrieval (ISMIR), pp 495–500Puckette M (1995) Score following using the sung voice. In: Proc. of the International Computer Music Conference (ICMC), pp 175–178Duan Z, Pardo B (2011) Soundprism: an online system for score-informed source separation of music audio. IEEE J. Sel. Top. Signal Process. 5(6):1205–1215Cont A (2006) Realtime audio to score alignment for polyphonic music instruments using sparse non-negative constraints and hierarchical hmms. In: Proc. of IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), ToulouseCuvillier P, Cont A (2014) Coherent time modeling of Semi-Markov models with application to realtime audio-to-score alignment. In Proc. of the 2014 IEEE International Workshop on Machine Learning for Signal Processing, p 16Joder C, Essid S, Richard G (2013) Learning optimal features for polyphonic audio-to-score alignment. IEEE Trans. Audio Speech Lang. Process. 21(10):2118–2128Dixon S (2005) Live tracking of musical performances using on-line time warping. In: Proc. International Conference on Digital Audio Effects (DAFx), Madrid, pp 92–97Hu N, Dannenberg RB, Tzanetakis G (2009) Polyphonic audio matching and alignment for music retrieval. In: Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp 185–188Orio N, Schwarz D (2001) Alignment of monophonic and polyphonic music to a score. In: Proc. International Computer Music Conference (ICMC)Alonso P, Cortina R, Rodríguez-Serrano FJ, Vera-Candeas P, Alonso-Gonzalez M, Ranilla J (2016) Parallel online time warping for real-time audio-to-score alignment in multi-core systems. J. Supercomput. doi: 10.1007/s11227-016-1647-5 (published online)Carabias-Ortí JJ, Rodríguez-Serrano FJ, Vera-Candeas P, Cañadas-Quesada FJ, Ruiz-Reyes N (2013) Constrained non-negative sparse coding using learnt instrument templates for realtime music transcription, Eng. Appl. Artif. Intell. 26(7):1671–1680Carabias-Ortí JJ, Rodríguez-Serrano FJ, Vera-Candeas P, Martínez-Muñoz D (2016) Tempo driven audio-to-score alignment using spectral decomposition and online dynamic time warping. ACM Trans. Intell. Syst. Technol. (accepted)FFTW (2016) http://www.fftw.org . Accessed July 2016NVIDIA CUDA Fast Fourier Transform library (cuFFT) (2016) http://developer.nvidia.com/cufft . Accessed July 2016The OpenMP API specification for parallel programming (2016) http://openmp.org . Accessed July 201

Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.Comment: ISMIR 201

Score-Informed Source Separation for Musical Audio Recordings [An overview]

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