Molina-Solana M.,ETSIIT |
Lluis Arcos J.,Artificial Intelligence Research Institute IIIA |
Gomez E.,University Pompeu Fabra
Intelligent Data Analysis | Year: 2010
Understanding the way performers use expressive resources of a given instrument to communicate with the audience is a challenging problem in the sound and music computing field. Working directly with commercial recordings is a good opportunity for tackling this implicit knowledge and studying well-known performers. The huge amount of information to be analyzed suggests the use of automatic techniques, which have to deal with imprecise analysis and manage the information in a broader perspective. This work presents a new approach, Trend-based modeling, for identifying professional performers in commercial recordings. Concretely, starting from automatically extracted descriptors provided by state-of-the-art tools, our approach performs a qualitative analysis of the detected trends for a given set of melodic patterns. The feasibility of our approach is shown for a dataset of monophonic violin recordings from 23 well-known performers. © 2010 - IOS Press and the authors. All rights reserved.
Olivares A.,Networking and Communications |
Olivares G.,ETSIIT |
Mula F.,ETSIIT |
Gorriz J.M.,Networking and Communications |
Ramirez J.,Networking and Communications
Journal of Systems Architecture | Year: 2011
Human body movement can be monitored through a wireless network composed of inertial sensors. This work presents the development of Wagyromag (Wireless Accelerometer, GYROscope and MAGnetometer), a wireless Inertial Measurement Unit (IMU) composed of a triaxial accelerometer, gyroscope and magnetometer. Communication is based on a 802.15.4 network. Furthermore, calibration, signal conditioning and signal processing algorithms are presented throughout this work. Wagyromag's high potential permits its application in a wide range of medical applications such as telerehabilitation, nocturnal epilepsy seizure detection, fall detection and other applications in the field of sport science. © 2011 Elsevier B.V. All rights reserved.
Barranco F.,ETSIIT |
Diaz J.,ETSIIT |
Pino B.,ETSIIT |
Journal of Visual Communication and Image Representation | Year: 2012
This paper presents a novel hardware-friendly motion estimation for real-time applications such as robotics or autonomous navigation. Our approach is based on the well-known Lucas & Kanade local algorithm, whose main problem is the unreliability of its estimations for large-range displacements. This disadvantage is solved in the literature by adding the sequential multiscale-with-warping extension, although it dramatically increases the computational cost. Our choice is the implementation of a multiresolution scheme that avoids the warping computation and allows the estimation of large-range motion. This alternative allows the parallel computation of the scale-by-scale motion estimation which makes the whole computation lighter and significantly reduces the processing time compared with the multiscale-with-warping approach. Furthermore, this last fact also means reducing the hardware resource cost for its potential implementation in digital hardware devices such as GPUs, ASICs, or FPGAs. In the discussion, we analyze the speedup of the multiresolution approach compared to the multiscale-with-warping scheme. For an FPGA implementation, we obtain a reduction of latency between 40% and 50% and a resource reduction of 30%. The final solution copes with large-range motion estimations with a simplified architecture very well-suited for customized digital hardware datapath implementations as well as current multicore architectures. © 2012 Elsevier Inc. All rights reserved.