A pipeline structure for the block QR update in digital signal processing

[EN] There exist problems in the field of digital signal processing, such as filtering of acoustic signals that require processing a large amount of data in real time. The beamforming algorithm, for instance, is a process that can be modeled by a rectangular matrix built on the input signals of an acoustic system and, thus, changes in real time. To obtain the output signals, it is required to compute its QR factorization. In this paper, we propose to organize the concurrent computational resources of a given multicore computer in a pipeline structure to perform this factorization as fast as possible. The pipeline has been implemented using both the application programming interface OpenMP and GrPPI, a library interface to design parallel applications based on parallel patterns. We tackle not only the performance challenge but also the programmability of our idea using parallel programming frameworks.This work was supported by the Spanish Ministry of Economy and Competitiveness under MINECO and FEDER projects TIN2014-53495-R and TEC2015-67387-C4-1-R.Dolz, MF.; Alventosa, FJ.; Alonso-Jordá, P.; Vidal Maciá, AM. (2019). A pipeline structure for the block QR update in digital signal processing. The Journal of Supercomputing. 75(3):1470-1482. https://doi.org/10.1007/s11227-018-2666-1S14701482753Huang Y, Benesty J, Chen J (2006) Acoustic MIMO signal processing (signals and communication technology). Springer, BerlinRamiro C, Vidal AM, González A (2015) MIMOPack: a high performance computing library for MIMO communication systems. J Supercomput 71:751–760Alventosa FJ, Alonso P, Piñero G, Vidal AM (2016) Implementation of the Beamformer algorithm for the NVIDIA Jetson. In: Actas de la Conferencia, Granada, Spain, pp 201–211. ISBN 978-3-319-49955-0Alventosa FJ, Alonso P, Vidal AM, Piñero G, Quintana-Ortí ES (2018) Fast block QR update in digital signal processing. J Supercomput. https://doi.org/10.1007/s11227-018-2298-5del Rio D, Dolz MF, Fernández J, García JD (2017) A generic parallel pattern interface for stream and data processing. Concurr Comput Pract Exp 29(24):e4175Benesty J, Chen J, Huang Y, Dmochowski J (2007) On microphone-array Beamforming from a MIMO acoustic signal processing perspective. IEEE Trans Audio Speech Lang Process 15(3):1053–1065Lorente J, Piñero G, Vidal AM, Belloch JA, González A (2011) Parallel implementations of Beamforming design and filtering for microphone array applications. In: 19th European Signal Processing Conference (EUSIPCO), Barcelona, Spain, pp 501–505Belloch JA, Ferrer M, González A, Martínez-Zaldívar FJ, Vidal AM (2013) Headphone-based virtual spatialization of sound with a GPU accelerator. J Audio Eng Soc 61:546–561Belloch JA, González A, Martínez-Zaldívar FJ, Vidal AM (2011) Real-time massive convolution for audio applications on GPU. J Supercomput 58(3):449–457Golub GH, Van Loan CF (2013) Matrix computations. Johns Hopkins studies in the mathematical sciences. Johns Hopkins University Press, BaltimoreGunter BC, van de Geijn RA (2005) Parallel out-of-core computation and updating the QR factorization. ACM Trans Math Softw 31(1):60–78Buttari A, Langou J, Kurzak J, Dongarra J (2009) A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Comput 35(1):38–53Dolz MF, Alventosa FJ, Alonso-Jordá P, Vidal AM (2018) A pipeline for the QR update in digital signal processing. In: Proceedings of the 18th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE 2018), Rota, Cádiz, Spain, pp 1–5Quintana-Ortí G, Quintana-Ortí ES, Van De Geijn RA, Van Zee FG, Chan E (2009) Programming matrix algorithms-by-blocks for thread-level parallelism. ACM Trans Math Softw 36(3):14:1–14:2

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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A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions

Network steganography conceals the transfer of sensitive information within unobtrusive data in computer networks. So-called micro protocols are communication protocols placed within the payload of a network steganographic transfer. They enrich this transfer with features such as reliability, dynamic overlay routing, or performance optimization --- just to mention a few. We present different design approaches for the embedding of hidden channels with micro protocols in digitized audio signals under consideration of different requirements. On the basis of experimental results, our design approaches are compared, and introduced into a protocol engineering approach for micro protocols.Comment: 20 pages, 7 figures, 4 table

Singing synthesis with an evolved physical model

A two-dimensional physical model of the human vocal tract is described. Such a system promises increased realism and control in the synthesis. of both speech and singing. However, the parameters describing the shape of the vocal tract while in use are not easily obtained, even using medical imaging techniques, so instead a genetic algorithm (GA) is applied to the model to find an appropriate configuration. Realistic sounds are produced by this method. Analysis of these, and the reliability of the technique (convergence properties) is provided

Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks