Srinivasa Murthy, Y. and Koolagudi, S.G. (2015) Classification of Vocal and Non-Vocal Regions from Audio Songs Using Spectral Features and Pitch Variations. IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, 3-6 May 2015, 1271-1276.
http://dx.doi.org/10.1109/ccece.2015.7129461

has been cited by the following article:

• TITLE: An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals

  AUTHORS: Muthumari Arumugam, Mala Kaliappan

  KEYWORDS: Audio Signal, Enhanced Mel Frequency Cepstral Coefficient (EMFCC), Enhanced Power Normalized Cepstral Coefficients (EPNCC), Probabilistic Neural Network (PNN) Classifier

  JOURNAL NAME: Circuits and Systems, Vol.7 No.4, April 26, 2016

  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, featureextraction 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.