Luiz Biscainho

Luiz Biscainho
Federal University of Rio de Janeiro | UFRJ · PEE/COPPE & DEL/Poli

25.16
 · 
DSc

About

105
Publications
7,952
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609
Citations
Introduction
Born in Rio de Janeiro, Brazil, in 1962. Electronics Engineer (1985), MSc (1990) and DSc (2000) in Electrical Engineering degrees from the Federal University of Rio de Janeiro (UFRJ). Worked in the telecommunication industry between 1985 and 1993. Currently Associate Professor at the Dept. of Electronics and Computer Engineering and the Electrical Engineering Postgrad. Program at UFRJ. Main research area: digital audio processing. Member of the IEEE, the AES, the SBrT, and the SBC.
Research Experience
September 2004 - present
Federal University of Rio de Janeiro
Position
  • Associate Professor
Description
  • Postgraduate teaching and supervision
January 2001 - present
Federal University of Rio de Janeiro
Position
  • Associate Professor
Description
  • Audio Processing Group (GPA) / Signals, Multimedia and Telecommunications Lab (SMT)
September 1993 - present
Federal University of Rio de Janeiro
Position
  • Associate Professor
Description
  • Undergraduate teaching and supervision
Education
March 1994 - December 2000
Federal University of Rio de Janeiro
Field of study
  • Electrical Engineering (DSc)
March 1988 - April 1990
Federal University of Rio de Janeiro
Field of study
  • Electrical Engineering (MSc)
March 1980 - February 1985
Federal University of Rio de Janeiro
Field of study
  • Electronics Engineering (BSc)

Publications

Publications (105)
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Chapter
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Network

Cited By

Projects

Projects (4)
Archived project
Project
The development of computer hardware technology and the proliferation of online music collections have sustained the development of Artificial Intelligence techniques for music research in several directions, fostering new interdisciplinary research opportunities. This interdisciplinary research project aims to develop innovative technological and music-analytical methods to gain fresh insight into the understanding and modeling of the rhythmic/metrical structure in audio recordings of expressive music performances. For this, we will explore the use of some new frameworks developed in the statistical relational learning area that have recently opened perspectives to model the complex relational structure of musical data. While the approaches we propose are common to any style of music, we exemplify our methods via an analysis of new datasets of Latin American music, bringing new musicological insight into some musical genres that have not yet been explored by the Music Information Retrieval research community. We will also provide the music research community with new annotated data and software resources.