ADAPTIVE AND NONLINEAR SIGNAL PROCESSING

1996/1997X Ciclo1967Versione digitalizzata della tesi di dottorato cartacea

Various nonlinear models and their identification, equalization and linearization

System identification is a pre-requisite to analysis of a dynamic system and design of an appropriate controller for improving its performance. The more accurate the mathematical model identified for a system, the more effective will be the controller designed for it. The identification of nonlinear systems is a topic which has received considerable attention over the last two decades. Generally speaking, when it is difficult to model practical systems by mathematical analysis method, system identification may be an efficient way to overcome the shortage of mechanism analysis method. The goal of the modeling is to find a simple and efficient model which is in accord with the practical system. In many cases, linear models are not suitable to present these systems and nonlinear models have to be considered. Since there are nonlinear effects in practical systems, e.g. harmonic generation, intermediation, desensitization, gain expansion and chaos, we can infer that most control systems are nonlinear. Nonlinear models are more widely used in practice, because most phenomena are nonlinear in nature. Indeed, for many dynamic systems the use of nonlinear models is often of great interest and generally characterizes adequately physical processes over their whole operating range. Thus, accuracy and performance of the control law increase significantly. Therefore, nonlinear system modeling is much more important than linear system identification. We will deal with various nonlinear models and their processing

Automatic Optimal Input Command for Linearization of cMUT Output by a Temporal Target

2014 IEEE. Reprinted, with permission, from Sébastien Ménigot, Dominque Certon, Dominque Gross and Jean-Marc Girault, Automatic Optimal Input Command for Linearization of cMUT Output by a Temporal Target, 2014 IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the Université François Rabelais de Tours' products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected] audienceCapacitive micromachined ultrasonic transducers (cMUTs) are a promising alternative to the piezoelectric transducer. However, their native nonlinear behavior is a limitation for their use in medical ultrasound applications. Several methods based on the pre-compensation of a preselected input voltage have been proposed to cancel out the harmonic components generated. Unfortunately, these existing pre-compensation methods have two major flaws. The first is that the pre-compensation procedure is not generally automatic, and the second is that they can only reduce the second harmonic component. This can, therefore, limit their use for some imaging methods, which require a broader bandwidth, e.g., to receive the third harmonic component. In this study, we generalized the presetting methods to reduce all nonlinearities in the cMUT output. Our automatic pre-compensation method can work whatever the excitation waveform. The precompensation method is based on the nonlinear modeling of harmonic components from a Volterra decomposition in which the parameters are evaluated by using a Nelder-Mead algorithm. To validate the feasibility of this approach, the method was applied to an element of a linear array with several types of excitation often encountered in encoded ultrasound imaging. The results showed that the nonlinear components were reduced by up to 21.2 dB

Blind identification of bilinear systems

Journal ArticleAbstract-This paper is concerned with the blind identification of a class of bilinear systems excited by non-Gaussian higher order white noise. The matrix of coefficients of mixed input-output terms of the bilinear system model is assumed to be triangular in this work. Under the additional assumption that the system output is corrupted by Gaussian measurement noise, we derive an exact parameter estimation procedure based on the output cumulants of orders up to four. Results of the simulation experiments presented in the paper demonstrate the validity and usefulness of our approach

Loudspeaker Modelling with Recurrent Neural Networks

Digital twins of loudspeakers are a useful assets for fine-tuning purposes during the design and the manufacturing phase. They can serve as an alternative to real-time measurement for objective evaluation of adjustments made by digital signal processing. Binaural loudspeaker models could introduce a more repeatable framework for subjective listening and provide flexibility for remote work due to the reduced need for actual physical devices. Neural Networks are a well-proven tool for system identification of different audio hardware devices. This thesis project will focus on creating a digital twin of a multimedia stereo loudspeaker system by using stereo audio waveform as the input and a binaural recording of the system's playback as the target waveform for Recurrent Neural Network (RNN) training. The RNN architecture is inspired by the current state-of-the-art method for single channel audio effects modelling, and is adapted for the stereo waveform use case. Firstly, the RNN model is tested with different synthesized target data that simulates the real recorded data. This approach allows us to estimate the properties which are the most challenging for the RNN to learn. Secondly, the experiments are run with a real recorded, time-aligned dataset, and the RNN's performance is objectively evaluated by the Error-To-Signal Ratio (ESR). In the current state-of-the-art method on single channel audio modelling, the initial hidden state of the RNN is computed by using no-gradient startup inference to accumulate the hidden state over the first few hundred samples of the training sequence. The thesis project proposes a new method called Discontinuous Sequence Training (DISCO). The method prepares the training dataset according to the RNNs architecture’s hyper-parameter sequence length and the system's impulse response length, such that it allows for correct initialization of the initial hidden state without additional pre-training inference. DISCO reaches the training and inference precision of hidden state initialization in the current state-of-the-art method for black-box modelling with RNNs only by modifying the dataset

Real-time Digital Simulation of Guitar Amplifiers as Audio Effects

Práce se zabývá číslicovou simulací kytarových zesilovačů, jakož to nelineárních analogových hudebních efektů, v reálném čase. Hlavním cílem práce je návrh algoritmů, které by umožnily simulaci složitých systémů v reálném čase. Tyto algoritmy jsou prevážně založeny na automatizované DK-metodě a aproximaci nelineárních funkcí. Kvalita navržených algoritmů je stanovana pomocí poslechových testů.The work deals with the real-time digital simulation of guitar amplifiers considered as nonlinear analog audio effects. The main aim is to design algorithms which are able to simulate complex systems in real-time. These algorithms are mainly based on the automated DK-method and the approximation of nonlinear functions. Quality of the designed algorithms is evaluated using listening tests.

Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle