Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Principled methods for mixtures processing

This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences

A collective intelligence framework for in silico representations of biomolecules and their activities.

The novel framework proposed in this thesis offers great potential for modelling the multi scale adaptive dynamics from molecules to cell at the physiological timescale. Most approaches for modelling biological phenomena focus on studies based on a specific instance of life, where specific biological problems are analysed. Mechanistic models based on universal principles will facilitate in developing general models for wider application in systems biology. The aim of the thesis is to investigate best approaches in representing biological complexity from molecules to cells and developing computational approaches to bring abstract theories to practical use by: (i) Identifying the major biomolecular self organising mechanism. (ii) Using a bottom-up integrative approach to model the internal organisation of the biological cell. (iii) Develop a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies. This thesis argues that a system theoretic approach based on Collective Intelligence where the concepts of self organisation and emergence underlie the approach is ideal to represent the multi scale and multi objective nature of the biological cell from the bottom up. This thesis proposes a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies on the collective behaviour of biomolecules