"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

Kuncheva, Ludmila I.

doi:10.1109/tfuzz.2003.819842

Cited by 132 publications

(66 citation statements)

References 42 publications

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“…Actually, it was shown in [Mar00] that the ordered weighted average, the WAM, and the partial minimum and maximum operators are all particular cases of FI with special FM. In fact, FI can be seen as a compromise between the evidence expressed by the outputs of the classification systems and the competence represented by the FM's knowledge of how the different information sources interact [Kun03].…”

Section: Fuzzy Integral and Fuzzy Measurementioning

confidence: 99%

Acoustic Event Detection and Classification

Temko

Nadeu

Macho

et al. 2009

Computers in the Human Interaction Loop

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The human activity that takes place in meeting-rooms or class-rooms is reflected in a rich variety of acoustic events, either produced by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Additionally, detection of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.Automatic detection and classification of acoustic events is the objective of this thesis work. It aims at processing the acoustic signals collected by distant microphones in meeting-room or classroom environments to convert them into symbolic descriptions corresponding to a listener's perception of the different sound events that are present in the signals and their sources.First of all, the task of acoustic event classification is faced using Support Vector Machine (SVM) classifiers, which are motivated by the scarcity of training data. A confusion-matrix-based variable-feature-set clustering scheme is developed for the multiclass recognition problem, and tested on the gathered database. With it, a higher classification rate than the GMM-based technique is obtained, arriving to a large relative average error reduction with respect to the best result from the conventional binary tree scheme. Moreover, several ways to extend SVMs to sequence processing are compared, in an attempt to avoid the drawback of SVMs when dealing with audio data, i.e. their restriction to work with fixed-length vectors, observing that the dynamic time warping kernels work well for sounds that show a temporal structure. Furthermore, concepts and tools from the fuzzy theory are used to investigate, first, the importance of and degree of interaction among features, and second, ways to fuse the outputs of several classification systems. The developed AEC systems are Two kinds of databases are used: two databases of isolated acoustic events, and a database of interactive seminars containing a significant number of acoustic events of interest. Our developed systems, which consist of SVM-based classification within a sliding window plus post-processing, were the only submissions not using HMMs, and each of them obtained competitive results in the corresponding evaluation.vi Speech activity detection was also pursued in this thesis since, in fact, it is a -especially important -particular case of acoustic event detection. An enhanced SVM training approach for the speech activity detection task is developed, mainly to cope with the problem of dataset reduction.The resulting SVM-based system is tested with several NIST Rich Transcription (RT) evaluation datasets, and it shows better scores than our GMM-based system, which ranked among the best systems in the RT06 evaluation.Finally, it is worth mentioning a few side outcomes from this thesis work. As it has been carried out in the framework of the CHIL EU project, the author has been responsible for the organization of the above m...

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Section: Fuzzy Integral and Fuzzy Measurementioning

confidence: 99%

Acoustic Event Detection and Classification

Temko

Nadeu

Macho

et al. 2009

Computers in the Human Interaction Loop

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“…In fact, FI can be seen as a compromise between the evidence expressed by the outputs of the classification systems and the competence represented by the FM's knowledge of how the different information sources interact [4].…”

Section: Fuzzy Integral and Fuzzy Measurementioning

confidence: 99%

“…The better the FM describes the real competence and interaction among all classification systems, the more accurate results can be expected. There are two methods of calculating the FM known to the authors (if it is not provided by an expert knowledge): one based on fuzzy densities [4], and the other based on learning the FM from training data [7] [8]. In our work, we have used the latter method: a supervised, gradient-based algorithm of learning the FM, with additional steps for smoothing the unmodified nodes:…”

Section: Fuzzy Integral and Fuzzy Measurementioning

confidence: 99%

“…Usual combinations of classifier outputs like sum, product, max, min, weighted arithmetical mean (WAM), etc [4], assume that each output represents an independent source of information that can be treated separately. Often, this is not the case, and an approach that considers the interactions among the classifier outputs is needed.…”

Section: Introductionmentioning

confidence: 99%

“…Often, this is not the case, and an approach that considers the interactions among the classifier outputs is needed. Over the past several years there have been a number of successful applications of the Fuzzy Integral (FI) [4] [5] in decision-making and pattern recognition using multiple information sources (e.g. [6] [7] [8]).…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

Temko

Macho

Nadeu

2008

Pattern Recognition

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Acoustic event classification may help to describe acoustic scenes and contribute to improve the robustness of speech technologies. In this work, fusion of different information sources with the Fuzzy Integral (FI), and the associated Fuzzy Measure (FM), are applied to the problem of classifying a small set of highly confusable human non-speech sounds. As FI is a meaningful formalism for combining classifier outputs that can capture interactions among the various sources of information, it shows in our experiments a significantly better performance than that of any single classifier entering the FI fusion module. Actually, that FI decision-level fusion approach shows comparable results to the highperforming SVM feature-level fusion and thus it seems to be a good choice when feature-level fusion is not an option. We have also observed that the importance and the degree of interaction among the various feature types given by the FM can be used for feature selection, and gives a valuable insight into the problem.Keywords: Acoustic event classification, audio features, fuzzy integral and fuzzy measure, feature-level and decision-level information fusion, feature selection, interaction of information sources, Choquet integral.

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2014

Combining Pattern Classifiers

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"Fuzzy" versus "nonfuzzy" in combining classifiers designed by boosting

Cited by 132 publications

References 42 publications

Acoustic Event Detection and Classification

Acoustic Event Detection and Classification

Fuzzy integral based information fusion for classification of highly confusable non-speech sounds

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