Speech/music classification using PLP and SVM
Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. This paper deals with the Speech/Music classification problem, starting from a set of features extracted directly from audio data. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work Perceptual Linear Prediction (PLP) features are extracted from the input signal. After feature extraction, classification is carried out, using Support Vector Model (SVM) model. The proposed feature extraction and classification models results in better accuracy in speech/music classification.
R.A. Redner and H.F. Walker, “Mixture Densities, Maximum Likelihood and the EM Algorithm,” SIAM Review, vol. 26, pp. 195-239, 1984.
C. Panagiotakis and G. Tziritas. A speech/music discriminator based on rms and zero-crossings,.IEEE Trans. Multimedia, 7(5):155–156, February 2005.
Peter M. Grosche, Signal Processing Methods for Beat Tracking, Music Segmentation and Audio Retrieval, Thesis, Universit¨at des Saarlandes, 2012.
PetrMotlcek, Modeling of Spectra and Temporal Trajectories in Speech Processing, PhD thesis, Brno University of Technology, 2003.
Poonam Sharma and Anjali Garg. Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks. International Journal of Computer Applications 142(7):12-17, May 2016.
Chungsoo Lim Mokpo, Yeon-Woo Lee, and Joon-Hyuk Chang, “New Techniques for Improving the practicality of a SVM-Based Speech/Music Classifier,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1657-1660, 2012.
Hongchen Jiang, JunmeiBai, Shuwu Zhang, and Bo Xu, “SVM-Based Audio Scene Classification,” IEEE International Conference Natural Language Processing and Knowledge Engineering, Wuhan, China, pp. 131-136, October 2005.
Lim and Chang, “Enhancing Support Vector Machine-Based Speech/Music Classification using Conditional Maximum a Posteriori Criterion,” Signal Processing, IET, vol. 6, no. 4, pp. 335-340, 2012.
Md. Al Mehedi Hasan and Shamim Ahmad. predSucc-Site: Lysine Succinylation Sites Prediction in Proteins by using Support Vector Machine and Resolving Data Imbalance Issue. International Journal of Computer Applications 182(15):8-13, September 2018.
Hend Ab. ELLaban, A A Ewees and Elsaeed E AbdElrazek. A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier. International Journal of Computer Applications 159(8):23-29, February 2017.