José a. Osuna

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Jos A. OsunaThe Recognition ofAcoustical Alarm SignalswithCellular Neural NetworksHartung-Gorre Verlag Konstanz1995Diss. ETH No. 11058The Recognition ofAcoustical Alarm SignalswithCellular Neural NetworksA dissertation submitted to theSWISS FEDERAL INSTITUTE OF TECHNOLOGYZURICHfor the degree ofDoctor of Engineering Sciencespresented byJOS A. OSUNAdipl. El.-Ing. ETHborn May 6, 1965citizen of Spainaccepted on the recommendation ofProf. Dr. G. S. Moschytz, examinerProf. Dr. M. Hasler, co-examiner2010A mis AbuelosConsuelo y Victorinoquienes iniciaron todo.AcknowledgmentsIt was in late September 1991 after the ECCTD-91 in Kopenhagen1thatProf. Moschytz suggested I have a look at two interesting papers. He had metProf. Chua at the conference who had told him about the Cellular Neural Net-works and Prof. Moschytz immediately foresaw their potential for signal pro-cessing. I read the two introductory papers with great interest; this was theinitiation of this thesis.I mention the anecdote above because it is typical for the way in whichProf. Moschytz guided my thesis during the time at the laboratory: he alwaysmade the right suggestions giving me very precious freedom in what I nallydecided to do. I amvery thankful to himfor this valuable working environment,but also for his warm-hearted conduct.Prof. Hasler spontaneously agreed, in spite of the short deadline, to be co-examiner. His technical comments have improved the quality of this thesis. Iam indebted to him for carefully reading through my work.Prof. Chuas enthusiasm for his Cellular Neural Networks is immense. Itis due to his push last year during his three-month stay at our laboratory thatI did not give up trying to nd a solution for the classication task. I thankhim also for having made such fascinating research possible by inventing theCellular Neural Networks.Prof. Roska invited me for a three-month stay at the Hungarian Academyof Sciences in spring 1993. I gratefully acknowledge his interest in my pro-ject. I also wish to thank the people from his group for their friendly support,especially Tibor Kozek for the e-mail contact we had after I left Budapest.1European Conference on Circuit Theory and DesignVIIVIII AcknowledgmentsI was very fortunate to have the opportunity to share the ofce with Gus-ti Kaelin before he was elected Professor. He taught me in long discussionshow to approach scientic problems. His modest way of presenting excellentsolutions served me to evaluate other peoples work.Thanks go to all members of the laboratory. It is a great pleasure to workin a place where everyone spontaneously helps the others. Dr. Max Dnkideserves special mention because most of my knowledge on computer systemsoriginates from his deep insight into almost everything related to computers.My sincere thanks are also extended to two student groups who acceleratedmy thesis by completing excellent semester projects: Ivo Hasler and Bjrn Tie-mann developed the fastest and most exible simulator of Cellular Neural Net-works, and Justus Bernold and Martin Hnggi investigated several methods onhow to process the transformed acoustical signals with Cellular Neural Net-works.I would like to thank Dr. Urs Mller, Michael Kocheisen, Bernhard Bumleand the other members of the Electronics Laboratory who gave me their sup-port concerning NeuroBasic and the MUSIC parallel computer. My thanksare due to Dr. Xavier Arreguit of the Centre Suisse dElectronique et de Mi-crotechnique (CSEM) in Neuchtel for his assistance on the binaural chip.Drahoslav Lms help was twofold: he revised the whole thesis by correc-ting my English, and he provided me with his LATEX2 distribution for the Sunworkstations at the laboratory and my Linux box at home. Without his fast andthorough corrections I would not had been able to keep to the deadline. Mythanks to him for his priceless help.I do not want to miss the opportunity of thanking my parents for encou-raging me to follow my interests. They always supported me in all aspectsconcerning my education. Their efforts led to a free choice in what I couldwork on after attaining my engineering diploma.Finally, I wish to express my loving gratitude to my wife Ruth and mychildren Rafael, Ester and Pablo for their patience and understanding in allthese days and nights they had to do without a normal family life.AbstractThis thesis investigates Cellular Neural Networks (CNNs), especially, theircapabilities for signal processing. A CNN is a time-continuous, nonlinear dy-namical system which is composed of locally interconnected cells that workin parallel. Analogue chips based on this structure are presently being develo-ped for use in applications where sophisticated signal processing at low powerconsumption is required.We organized this thesis into two parts. The rst part presents some theo-retical results on CNNs. The second part presents the solution of an acoustical-signal classication problem, thereby showing a possible application of CNNsin hearing aids.Part I consists of three chapters. In the rst, we introduce the basic CNNstructure. Chapter 2 presents a method for the determination of the networkparameters for a certain class of CNNs. Finally, in Chapter 3 we give lowerand upper bounds on the number of different tasks that can be performed byCNNs with binary input values.Chapter 4 gives an overview of the contents of Part II. The problem of therecognition of given sets of acoustical alarm signals is stated, and its solutionwith CNNs is briey discussed. The rst section of Chapter 5 deals with thepreprocessing stage needed to transform the signals under consideration intoimages in order to provide inputs suitable for the CNN. The phase-preservingtransformation considered there may well be performed by a CNN as well,as demonstrated in the second section of Chapter 5, which gives the solutionfor the realization of arbitrary second-order linear lters with CNNs. Next,Chapter 6 presents three processing methods on how to generate a characteris-tic image for every given acoustical alarm signal: different image-processingIXX Abstractsteps and a simple timetospace mapping are used in every method. The re-sulting characteristic images are classied by a one-layer perceptron (Chap-ter 7). Thus, the idea of using CNNs for the classication of acoustical alarmsignals is to transform the problem into one of image classication.In Chapter 8 we mention the classication skills of healthy people, and listthe results achieved with our system. A discussion of the results can be foundin the last chapter. By comparing the performance of our systemto that of otherschemes we can state that, rst, CNNs may well be used for the recognitionof acoustical alarm signals in hearing aids, and, second, CNNs are capable ofprocessing non-stationary signals.In the appendix we present the tools used to simulate our system. Theexible and easytouse simulator of CNNs runs on a parallel computer andpresently achieves the fastest iteration speed of simulators reported in recentliterature.KurzfassungDie vorliegende Dissertation handelt von zellularen neuronalen Netzwer-ken, kurz CNN (Cellular Neural Networks), und im speziellen von deren Ein-satz fr die Verarbeitung nichtstationrer Signale. Ein CNN ist ein zeitkonti-nuierliches, nichtlineares, dynamisches System, das aus parallel arbeitendenZellen besteht, welche lokal miteinander verbunden sind, d.h., nur Verknp-fungen zwischen benachbarten Zellen aufweisen. Analoge integrierte Schal-tungen, die auf dieser Struktur basieren, sind weltweit Gegenstand laufenderForschungsarbeiten. Es wird das Ziel verfolgt, die CNN in Anwendungen zumEinsatz kommen zu lassen, welche hohe Signalverarbeitungsgeschwindigkei-ten bei geringer Leistungsaufnahme voraussetzen.Die Abhandlung ist in zwei Teile gegliedert. Im ersten Teil werden theore-tische Resultate zu den CNN prsentiert, im zweiten Teil wird diese Netzwerk-struktur fr die Klassikation akustischer Alarmsignale eingesetzt. Es wird da-mit gezeigt, dass Hrgerte ein mgliches Anwendungsgebiet der CNN sind.Teil I besteht aus drei Kapiteln. Im ersten wird der Aufbau der CNN vor-gestellt. Kapitel 2 zeigt, wie die Netzwerkparameter fr eine spezielle CNN-Klasse ermittelt werden knnen. Schliesslich werden in Kapitel 3 untere undobere Schranken fr die Anzahl verschiedener Aufgaben angegeben, die mitden CNN bei zweiwertigen Eingangsdaten gelst werden knnen.Kapitel 4 gibt eine bersicht zu Teil II. Das Problem der Erkennung vorge-gebener Klassen akustischer Alarmsignale wird formuliert und dessen CNN-Lsung skizziert. Der erste Abschnitt von Kapitel 5 behandelt die Vorverar-beitung der betrachteten Signale. Diese werden zu Bildern umgewandelt, umeine fr die CNN geeignete Darstellung der Eingangsdaten zu bekommen. Dieverwendete, phasenerhaltende Abbildung kann mit den CNN selbst vorgenom-XIXII Kurzfassungmen werden, wie im zweiten Abschnitt von Kapitel 5 anhand der Realisierungbeliebiger linearer Filter zweiter Ordnung mit einer CNN-kompatiblen Struk-tur gezeigt wird. Kapitel 6 stellt drei Verarbeitungsmethoden zur Generierungeines charakteristischen Bildes fr jedes gegebene akustische Alarmsignal vor.Dabei werden verschiedene Bildverarbeitungsschritte und eine einfache Um-wandlung der Zeitachse in eine rtliche Dimension vorgenommen. Die resul-tierenden charakteristischen Bilder werden mit einem Einschichtperceptronklassiert (Kapitel 7). Die CNN werden also dazu verwendet, das Problem derErkennung akustischer Alarmsignale auf eine Bildklassikation zu fhren.In Kapitel 8 wird anhand von Untersuchungen gezeigt, wie gut der nichthrgeschdigte Mensch die akustischen Alarmsignale erkennt, und die Erken-nungsfehlerraten des vorgeschlagenen Systems werden prsentiert. Die Be-sprechung der Resultate erfolgt im letzten Kapitel. Durch den Vergleich mitanderen Klassikationsmethoden kann erstens festgehalten werden, dass sichdie CNN anbieten, die Klassikation akustischer Alarmsignale in Hrgertezu implementieren.