Abstract not found

The ability of a normal human listener to recognize objects in the environment from only the sounds they produce is extraordinarily robust with regard to characteristics of the acoustic environment and of other competing sound sources. In contrast, computer systems designed to recognize sound sources function precariously, breaking down whenever the target sound is degraded by reverberation, noise, or competing sounds. Robust listening requires extensive contextual knowledge, but the potential contribution of sound-source recognition to the process of auditory scene analysis has largely been neglected by researchers building computational models of the scene analysis process. This thesis proposes a theory of sound-source recognition, casting recognition as a process of gathering information to enable the listener to make inferences about

Support Vector Machines (SVMs) represent a new approach to pattern classification which has recently attracted a great deal of interest in the machine learning community. Their appeal lies in their strong connection to the underlying statistical learning theory, in particular the theory of Structural Risk Minimization. SVMs have been shown to be particularly successful in fields such as image identification and face recognition; in many problems SVM classifiers have been shown to perform much better than other nonlinear classifiers such as artificial neural networks and k-nearest neighbors. This paper explores the issues involved in applying SVMs to phonetic classification as a first step to speech recognition. We present results on several standard vowel and phonetic classification tasks and show better performance than Gaussian mixture classifiers. We also present an analysis of the difficulties we foresee in applying SVMs to continuous speech recognition problems. 1. INTRODUCTION ...

Finding the "right" number of clusters, k, for a data set is a difficult, and often ill-posed, problem. In a probabilistic clustering context, likelihood-ratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new cross-validated likelihood criterion is investigated for determining cluster structure. A practical clustering algorithm based on Monte Carlo crossvalidation (MCCV) is introduced. The algorithm permits the data analyst to judge if there is strong evidence for a particular k, or perhaps weaker evidence over a sub-range of k values. Experimental results with Gaussian mixtures on real and simulated data suggest that MCCV provides genuine insight into cluster structure. v-fold cross-validation appears inferior to the penalized likelihood method (BIC), a Bayesian algorithm (AutoClass v2.0), and the new MCCV algorithm. Overall, MCCV and AutoClass appear the most reliable of the methods. MCCV provides the data-miner with a usefu...

One of the key problems in supervised learning is the insufficient size of the training set. The natural way for an intelligent learner to counter this problem and successfully generalize is to exploit prior information that may be available about the domain or that can be learned from prototypical examples. We discuss the notion of using prior knowledge by creating virtual examples and thereby expanding the effective training set size. We show that in some contexts, this idea is mathematically equivalent to incorporating the prior knowledge as a regularizer, suggesting that the strategy is well-motivated. The process of creating virtual examples in real world pattern recognition tasks is highly non-trivial. We provide demonstrative examples from object recognition and speech recognition to illustrate the idea. 1 Learning from Examples Recently, machine learning techniques have become increasingly popular as an alternative to knowledge-based approaches to artificial intelligence pro...

rely almost exclusively on the acoustic speech signal and, consequently, these systems often perform poorly in noisy environments [I]. Attempts to clean up the acoustic input have had limited success [2]. Another approach is to use other sources of speech information, such as visual speech signals. The perception of acoustic speech by humans can be affected by the visible speech signals [3-51. Specifically, when the acoustic signal is degraded by noise, the visual signal can provide supplemental speech information that improves speech perception [6-81. When no acoustic signal is available, as for the profoundly deaf, the visual signal alone can provide speech information through lip reading [9- 1 I]. Here we answer two questions: Can the speech information conveyed by visual speech signals be extracted automatically? How can this information be combined with information from the acoustic signal to improve automat

Abstract not found

Abstract not found

Computer Music Journal is currently published by The MIT Press. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at

We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We derive a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.