Bruno Gas

UPMC Sorbonne Universités, ISIR, Faculty Member

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Papers by Bruno Gas

Adaptive parametric algorithms for processing coherent Doppler-lidar signal

IEEE Transactions on Geoscience and Remote Sensing, 1999

The authors study the autoregressive and moving average (ARMA) filter for lidar signal processing... more The authors study the autoregressive and moving average (ARMA) filter for lidar signal processing. After a short presentation of the atmospheric laser Doppler instrument project (ALADIN), they introduce the objective of this paper, which is to extract the Doppler frequency and to retrieve the spectral width of a noised lidar signal. A general presentation of ARMA filters and parametric adaptive algorithms (PAAs) is provided. Then they present results about the choice of the model, the Doppler frequency estimate, and the spectral width estimate. Finally, they study the possible estimate of SNR, which is biased by the first estimates (Doppler frequency and spectral width)

Filter Bank Design for Speaker Diarization Based on Genetic Algorithms

by Bruno Gas and Mohamed Chetouani

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best s... more Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. In this article we propose to use genetic algorithms to design a feature extraction method adapted to the speaker diarization task. We present an adaptation of the common MFCC feature extractor which consists in designing a filter bank, with optimized bandwidths. Experiments are carried out using a state-of-the-art speaker diarization system. The proposed method outperforms the original filter bank based on the Mel scale one. Furthermore, the obtained filter bank reveals the importance of some specific spectral information for speaker recognition

Non-Linear Predictors based on the Functionally Expanded Neural Networks for Speech Feature Extraction

by J-l Zarader, Bruno Gas, and Mohamed Chetouani

2006 IEEE International Conference on Engineering of Intelligent Systems, 2006

In this paper we focus on the design of the feature extractor stage of the speech recognition sys... more In this paper we focus on the design of the feature extractor stage of the speech recognition system which aims to compute optimal vectors for the next phoneme classification stage. We propose a new non-linear feature extraction method based on the linear-in-parameters functionally expanded neural network (FENN) model. The main idea is to design an improved and flexible feature extractor

A New Approach To Speech Coding: the Neural Predictive Coding

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000

Nonlinear and discriminant feature extraction applied to phonemes recognition

by Bruno Gas and Mohamed Chetouani

Traitement du Signal

Language detection by neural discrimination

Multi Filter Bank Approach for Speaker Verification Based on Genetic Algorithm

by Bruno Gas, Mohamed Chetouani, and D. O'Shaughnessy

Lecture Notes in Computer Science, 2007

After the success of NOLISP'03, NOLISP'04 summer school and NOLISP'05, we are pleased to present ... more

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New Sub-band Processing Framework Using Non-linear Predictive Models for Speech Feature Extraction

by Bruno Gas and Mohamed Chetouani

Lecture Notes in Computer Science, 2006

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Extracting space dimension information from the auditory modality sensori-motor flow using a bio-inspired model of the cochlea

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

First task robots have to realise is sensing and acting in the environment. Can a robot learn the... more First task robots have to realise is sensing and acting in the environment. Can a robot learn the way it is able to sense and act in the world without any hardwired notions? Is it able to learn it from the only data he has access to, that is high-dimension sensory inputs and motor outputs? This paper presents experimental results

Non-linear Speech Feature Extraction for Phoneme Classification and Speaker Recognition

by Marcos Faundez-Zanuy, Bruno Gas, and Mohamed Chetouani

Lecture Notes in Computer Science, 2005

In this paper we propose a new feature extraction algorithm based on non-linear prediction: the N... more In this paper we propose a new feature extraction algorithm based on non-linear prediction: the Neural Predictive Coding (NPC) model which is an extension of the classical LPC one. We apply this model to two significant tasks: phoneme classification and speaker identification. For the first one, the NPC model is trained with a Minimum Classification Error (MCE) criterion. The experiments

Learning an internal representation of the end-effector configuration space

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

ABSTRACT Current machine learning techniques proposed to automatically discover a robot&#39;s... more ABSTRACT Current machine learning techniques proposed to automatically discover a robot&#39;s kinematics usually rely on a priori information about the robot&#39;s structure, sensor properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.

Sensorimotor learning of sound localization from an auditory evoked behavior

2012 IEEE International Conference on Robotics and Automation, 2012

A new method for self-supervised sensorimotor learning of sound source localization is presented,... more A new method for self-supervised sensorimotor learning of sound source localization is presented, that allows a simulated listener to learn an auditorimotor map from the sensorimotor experience provided by an auditory evoked behavior. The map represents the auditory space and is used to estimate the azimuthal direction of sound sources. The learning mainly consists in non-linear dimensionality reduction of sensorimotor data. Our results show that an auditorimotor map can be learned, both from real and simulated data, and that the online learning leads to accurate estimations of azimuthal sources direction.

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Space dimension perception from the multimodal sensorimotor flow of a naive robotic agent

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

Abstract Perception and action are fundamental tasks for autonomous robots. Traditionnally, they ... more

Learning agent’s spatial configuration from sensorimotor invariants

Robotics and Autonomous Systems, 2015

ABSTRACT The design of robotic systems is largely dictated by our purely human intuition about ho... more ABSTRACT The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving. The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants. One of the most fundamental perceptual notions, space, cannot be an exception to this requirement. In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot’s sensorimotor flow. We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment. The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs. In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent’s exteroceptors. We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina. The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina.

Binaural Systems in Robotics

The Technology of Binaural Listening, 2013

Audio/video fusion for objects recognition

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In mobile robotics applications, pattern and object recognition are mainly achieved relying only ... more In mobile robotics applications, pattern and object recognition are mainly achieved relying only on vision. Several other perceptual modalities are also available such as, touch, hearing or vestibular proprioception. They are rarely used and can provide valuable additional information within the recognition tasks. This article presents an analysis of several methods of fusion of perceptual and auditory modalitites. It relies

Discriminant neural predictive coding applied to phoneme recognition

by Bruno Gas and Mohamed Chetouani

In this article, we propose to study a speech coding method applied to the recognition of phoneme... more In this article, we propose to study a speech coding method applied to the recognition of phonemes. The model proposed (the neural predictive coding (NPC) and its three declinations NPC-1, NPC-2 and DFE-NPC) is a connectionist model (multilayer perceptron) based on the nonlinear prediction of the speech signal. We show that it is possible to improve the discriminant capacities of such an encoder with the introduction of signal membership class information as from the coding stage. As such, it ÿts in with the category of discriminant features extraction (DFE) encoders already proposed in literature. In this study we present a theoretical validation of the model in the hypothesis of unnoised signals and Gaussian noised signals. We also deÿne a new distance, the NPC distance, that will allow experimental validation of the model. NPC performances are compared to that obtained with traditional methods used to process speech on the Darpa Timit phoneme base. Simulations presented here show that the classiÿcation rates have clearly improved compared to usual methods, in particular regarding phonemes considered di cult to process (/b/,/d/,/g/ and /p/,/t/,/k/ phonemes).

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Complementary features for speaker verification based on genetic algorithms

by Bruno Gas and Mohamed Chetouani

Speech recognition systems usually need a feature extraction stage aiming at obtaining the best s... more Speech recognition systems usually need a feature extraction stage aiming at obtaining the best signal representation. State of the art speaker verification systems are based on cepstrals features like MFCC, LFCC or LPCC. In this article, we propose to use a genetic algorithm to provide new features able to complete the LFCC's.

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Pathological sit-to-stand predictive models for control of a rehabilitation robotic device

by Bruno Gas and Ludovic Saint-Bauzel

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, 2007

The aim of the work presented in this paper is to realize the model for the control of a robotic ... more

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Pathological Sit-To-Stand Models for Control of a Rehabilitation Robotic Device

by Ludovic Saint-Bauzel and Bruno Gas

2007 IEEE 10th International Conference on Rehabilitation Robotics, 2007

The aim of the work presented in this paper is to realise the model for the control of a robotic ... more The aim of the work presented in this paper is to realise the model for the control of a robotic interface for equilibrium assistance during sit-to-stand transfer of patients affected by posture pathology as Multiple Sclerosis and Post-Fall Syndrome. Interactive robotic devices designed as human-centered robotic devices, can give more comfortable and more efficient solutions than traditional technical devices. One supposes the need of a virtual model of the pathology. This model, called "observer", aims at being used in the smoothing control part of this assisting device. A useful property of this observer should be a postural prediction ability. This article presents a study of different solutions a Neural Trajectory Generator(NTG), a Linear Predictor(LP) and a Neural Predictive Observer (NPO). Records used for the learning have been done from healthy people that stand up normally and quickly. Some tests have also been done with patients affected by cerebellar syndrome Multiple Sclerosis disease. The presented experimental results show the good accuracy of these approaches whatever the speed of the movement.

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