Acoustic Event Detection Using Fuzzy Integral Ensemble and Oriented Fuzzy Local Binary Pattern Encoded CNN

In this paper, we propose a novel ensemble classifier using an Oriented Fuzzy Local Binary Pattern Encoded Convolutional Neural Network (CNN) for acoustic event detection (AED). The CNN has been widely used to perform acoustic event detection using a spectrogram image of the acoustic signals. The efficiency of the CNN depends on representation of the spectrogram images used during the training process. We propose the Oriented Fuzzy Local Binary Pattern (OFLBP) that extracts directional texture features from the spectrogram image by inspecting neighborhood pixels present at different angles from a central pixel. The proposed OFLBP technique is capable to deal with uncertainty present in the spectrogram image. The ensemble of the trained CNN is performed by a Fuzzy Integral method. The experiment and results show the proposed method outperforms to existing AED methods to classify the ESC-50 dataset.