Abstract
Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.
Highlights
In this paper, an efficient approach for segmentation, Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction and Probabilistic Neural Network (PNN)-based classification of audio signal is proposed
This paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals
True Positive (TP) is the number of audio signals that are correctly classified as a music or non-musical signal and False Negative (FN) is the number of music signals that are incorrectly classified as non-musical signal
Summary
An efficient approach for segmentation, EMFCC-EPNCC based feature extraction and PNN-based classification of audio signal is proposed. The background section presents a brief overview of the existing audio. How to cite this paper: Muthumari, A. and Mala, K. (2016) An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals. Feature extraction and classification techniques, along with its drawbacks. The proposed work is illustrated in the contribution section
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have