Classification of acoustic events using SVM-based clustering schemes

Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative. Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme.Peer Reviewe

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

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.Peer Reviewe

Acoustic event detection: SVM-based system and evaluation setup in CLEAR’07

In this paper, the Acoustic Event Detection (AED) system developed at the UPC is described, and its results in the CLEAR evaluations carried out in March 2007 are reported. The system uses a set of features composed of frequency-filtered band energies and perceptual features, and it is based on SVM classifiers and multi-microphone decision fusion. Also, the current evaluation setup and, in particular, the two new metrics used in this evaluation are presented.Peer ReviewedPostprint (author’s final draft

Classification of meeting-room acoustic events with Support Vector

Acoustic events produced in meeting-room-like environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of acoustic events, using and comparing several types of features and various classifiers based on either GMM or SVM. A variable-feature-set clustering scheme is developed and compared with an already reported binary tree scheme. In our experiments with event-level features, the proposed clustering scheme with SVM achieves a 31.5 % relative error reduction with respect to the best result from a binary tree scheme. 1

Classification of

acoustic events using SVM-based clustering scheme

Classification of acoustic events using SVM-based clustering schemes

Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative. Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme.Peer Reviewe

Classification of acoustic events using SVM-based clustering schemes

Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative. Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme.Peer Reviewe

approach

of features and combination of classifiers using a fuzz

Comparison of sequence discriminant support vector machines for acoustic event classification

In a previously reported work, classification techniques based on Support Vector Machines (SVM) showed a good performance in the task of acoustic event classification. SVM are discriminant classifiers, but they cannot easily deal with the dynamic time structure of sounds, since they are constrained to work with fixed-length vectors. Several methods that adapt SVM to sequence processing have been reported in the literature. In this paper, they are reviewed and applied to the classification of 16 types of sounds from the meeting room environment. With our database, we have observed that the dynamic time warping kernels work well for sounds that show a temporal structure, but the best average score is obtained with the Fisher kernel. 1