Content-Based Audio Classification and Retrieval Using Segmentation, Feature Extraction and Neural Network Approach

Abstract

The volume of audio data is increasing tremendously daily on public networks like Internet. This increases the difficulty in accessing those audio data. Hence, there is a need of efficient indexing and annotation mechanisms. Non-stationarity and discontinuity present in the audio signal rise the difficulty in segmentation and classification of audio signals. The other challenging task is to extract and select the optimal features in audio signal. The application areas of audio classification and retrieval system include speaker recognition, gender classification, music genre classification, environment sound classification, etc. This paper proposes a machine learning- and neural network-based approach which performs audio pre-processing, segmentation, feature extraction, classification and retrieval of audio signal from the dataset. We have proposed novel approach of classification and retrieval using FPNN by combining fuzzy logic and PNN characteristics. We found that FPNN classifier gives better accuracy, F1-score and Kappa coefficient values compared to SVM, k-NN and PNN classifiers.