Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio classification from time-frequency texture
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
Stress Detection Using Speech Spectrograms and Sigma-pi Neuron Units
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 02
Environmental sounds classification based on visual features
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Audio Denoising by Time-Frequency Block Thresholding
IEEE Transactions on Signal Processing
Using One-Class SVMs and Wavelets for Audio Surveillance
IEEE Transactions on Information Forensics and Security
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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This paper presents an approach aimed at recognizing environmental sounds for surveillance and security applications.We propose a robust environmental sound classification approach, based on spectrograms features derive from log-Gabor filters. This approach includes three methods. In the first two methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The third method uses the same steps but applied only to three patches extracted from each spectrogram.To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.