Open AccessSpeech/music classification using PLP and SVM
Author(s) -
Thiruven Gatanadhan R
Publication year - 2019
Publication title -
international journal of engineering and computer science
Language(s) - English
Resource type - Journals
ISSN - 2319-7242
DOI - 10.18535/ijecs.v8i02.4277
Subject(s) - computer science , support vector machine , search engine indexing , audio mining , audio signal , feature extraction , speech recognition , pattern recognition (psychology) , artificial intelligence , set (abstract data type) , scheme (mathematics) , mel frequency cepstrum , feature (linguistics) , speech coding , acoustic model , speech processing , mathematical analysis , linguistics , philosophy , mathematics , programming language
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.