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

Abstract

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.

Source ambiguity resolution of overlapped sounds in a multi-microphone room environment

“…For taking the assignment decision, the six sets of scores LLR 1 and LLR 2 , computed as indicated in Equation 1, are combined either with a uniformly weighted average [31] of the 12 values or by fuzzy integral-based fusion [19]. In the following, the latter technique is presented.…”

“…However, a more sophisticated weighting technique that considers all subsets of information sources: the fuzzy integral (FI) approach, is considered in this work [19].…”

“…In this work, a supervised gradient-based training algorithm for learning the fuzzy measures from the training data with cross-validation is used [18,19].…”

“…On the contrary, to that previous published work, in the PA system reported here, all the six microphone arrays available in the room are employed in the experiments. For taking the decision, the scores obtained from each array are combined using either a product of likelihood ratios or a fuzzy integral-based fusion technique [17][18][19]. Experiments are carried out for the scenario described above and with a set of signals collected in the smart-room.…”

A Retrospective Assessment of Fuzzy Logic Applications in Voice Communications and Speech Analytics

“…A close topic is that of acoustic event detectors; we notice the interesting approach in [45], where information fusion for classification of non-speech sounds is performed by a skilled use of fuzzy integrals. Similarly, FL-based techniques applied to speech segmentation have been proposed by many authors, but the penetration of these techniques in the mainstream of speech segmentation is still limited.…”