Fuzzy integral in multicriteria decision making
Fuzzy Sets and Systems - Special issue on fuzzy information processing
Time and frequency filtering of filter-bank energies for robust HMM speech recognition
Speech Communication - Special issue on noise robust ASR
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Classification of acoustic events using SVM-based clustering schemes
Pattern Recognition
Acoustic Event Detection: SVM-Based System and Evaluation Setup in CLEAR'07
Multimodal Technologies for Perception of Humans
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Acoustic event detection in meeting-room environments
Pattern Recognition Letters
Level-dependent Sugeno integral
IEEE Transactions on Fuzzy Systems
General Minkowski type inequalities for Sugeno integrals
Fuzzy Sets and Systems
An inequality related to Minkowski type for Sugeno integrals
Information Sciences: an International Journal
Hölder type inequality for Sugeno integral
Fuzzy Sets and Systems
Fuzzy posterior-probabilistic fusion
Pattern Recognition
Detecting laughter in spontaneous speech by constructing laughter bouts
International Journal of Speech Technology
Applications of fuzzy integrals for predicting software fault-prone
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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 high-performing 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.