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
Inclusion of Video Information for Detection of Acoustic Events Using the Fuzzy Integral
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
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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.