HReMAS: Hybrid Real-time Musical Alignment System

[EN] This paper presents a real-time audio-to-score alignment system for musical applications. The aim of these systems is to synchronize a live musical performance with its symbolic representation in a music sheet. We have used as a base our previous real-time alignment system by enhancing it with a traceback stage, a stage used in offline alignment to improve the accuracy of the aligned note. This stage introduces some delay, what forces to assume a trade-off between output delay and alignment accuracy that must be considered in the design of this type of hybrid techniques. We have also improved our former system to execute faster in order to minimize this delay. Other interesting improvements, like identification of silence frames, have also been incorporated to our proposed system.This work has been supported by the "Ministerio de Economia y Competitividad" of Spain and FEDER under Projects TEC2015-67387-C4-{1,2,3}-R.Cabañas-Molero, P.; Cortina-Parajón, R.; Combarro, EF.; Alonso-Jordá, P.; Bris-Peñalver, FJ. (2019). HReMAS: Hybrid Real-time Musical Alignment System. The Journal of Supercomputing. 75(3):1001-1013. https://doi.org/10.1007/s11227-018-2265-1S10011013753Alonso P, Cortina R, Rodríguez-Serrano FJ, Vera-Candeas P, Alonso-González M, Ranilla J (2017) Parallel online time warping for real-time audio-to-score alignment in multi-core systems. J Supercomput 73(1):126–138Alonso P, Vera-Candeas P, Cortina R, Ranilla J (2017) An efficient musical accompaniment parallel system for mobile devices. J Supercomput 73(1):343–353Arzt A (2016) Flexible and robust music tracking. 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Increasing data locality and introducing Level-3 BLAS in the Neville elimination

In this paper we present two new algorithmic variants to compute the Neville elimination, with and without pivoting, which improve data locality and cast most of the computations in terms of high-performance Level 3 BLAS. The experimental evaluation on a state-of-the-art multi-core processor demonstrates that the new blocked algorithms exhibit a much higher degree of concurrency and better cache usage, yielding higher performance while offering numerical accuracy akin to that of the traditional columnwise variant in most casesPedro Alonso, Raquel Cortina and José Ranilla were supported by project MICINN TIN2010-14971. Enrique S. Quintana-Ortí was supported by project CICYT TIN2008-06570-C04-01 and FEDE

Improving NNMFPACK with heterogeneous and efficient kernels for beta-divergence metrics

NnmfPack is a library for the nonnegative matrix factorization (NNMF) problem. Nowadays NNMF is an essential tool in many fields spanning machine learning, data analysis, image analysis or audio source separation, among others. NnmfPack is an efficient numerical library conceived for shared memory heterogeneous parallel systems, and it supports, from its conception, both conventional multi-core processors and many-core coprocessors. In this article, NnmfPack is extended to handle different metrics options ( ββ -divergence), and some other parallel algorithms have been added and tested. The performance of the new functionalities of NnmfPack is tested, and some precision results of the implementations are showed using an example borrowed from the image processing field.This work has been partially supported by "Ministerio de Economia y Competitividad" from Spain, under the projects TEC2012-38142-C04-01 and TEC2012-38142-C04-04 and by ISIC/2012/006 and PROMETEO FASE II 2014/003 projects of Generalitat Valenciana.Díaz-Gracia, N.; Cocaña-Fernández, A.; Alonso-González, M.; Martínez Zaldívar, FJ.; Cortina, R.; García Mollá, VM.; Alonso, P.... (2015). Improving NNMFPACK with heterogeneous and efficient kernels for ß-divergence metrics. Journal of Supercomputing. 71(5):1846-1856. https://doi.org/10.1007/s11227-014-1363-yS18461856715Battenberg E, Freed A, Wessel D (2010) Advances in the parallelization of music and audio applications. 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IEEE Trans Signal Process 60(6):2882–2898Cichocki A, Phan AH (2009) Fast local algorithms for large scale nonnegative matrix and tensor factorizations. In: Proceedings of IEICE transactions on fundamentals of electronics communications and computer sciences, E92-A, pp 708–721Cichocki A, Zdunek R, Amari SI (2007) Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization. In: Proceedings of the 7th international conference on independent component analysis and signal separation, September 9–12, London, UK. LNCS, vol 4666. Springer, Berlin, pp 169–176Alonso P, García VM, Martínez-Zaldívar FJ, Salazar A, Vergara L, Vidal AM (2014) Parallel approach to NNMF on multicore architecture. J Supercomput. 70(2):564–576Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Alonso P, Ranilla J, Vidal AM (2014) NNMFPACK: a versatile approach to an NNMF parallel library. In: Proceedings of the 2014 international conference on computational and mathematical methods in science and engineering, Cádiz, 2014, pp 456–465Carabias-Orti JJ, Rodriguez-Serrano FJ, Vera-Candeas P, Cañadas-Quesada FJ, Ruiz-Reyes N (2013) Constrained non-negative sparse coding using learnt instrument templates for real time music transcription. Eng Appl AI 26(7):1671–1680Carabias-Orti JJ, Virtanen T, Vera-Candeas P, Ruiz-Reyes N, Cañadas-Quesada FJ (2011) Musical instrument sound multi-excitation model for non-negative spectrogram factorization. IEEE J Select Topics Signal Process 5(6):1144–1158Minami M, Eguchi S (2002) Robust blind source separation by beta-divergence. Neural Comput 14:1859–1886Févotte C, Bertin N, Durrieu J-L (2009) Nonnegative matrix factorization with the Itakura-Saito divergence: with application to music analysis. Neural Comput 21:793–830Golub GH, Van Loan CF (1996) Matrix Comput. Johns Hopkins University Press, Baltimorehttp://pirserver.edv.uniovi.e