Overlapped Music segmentation using a new Effective Feature and Random Forests

<div><p>In the field of audio classification, audio signals may be broadly divided into three classes: speech, music and events. Most studies, however, neglect that real audio soundtracks can have any combination of these classes simultaneously. In this study, a novel feature, “Entrocy”, is proposed for the detection of music both in pure form and overlapping with the other audio classes. Entrocy is defined as the variation of the information (or entropy) in an audio segment over time. Segments which contain music were found to have lower Entrocy since there are fewer abrupt changes over time.</p></div><p class="Abstract">We have also compared Entrocy with existing music detection features and the entrocy showing a promising performance.</p><p class="IndexTerms"><a name="PointTmp"></a><em>Keywords</em>—Music detection, audio content analysis, audio indexing, Entropy, real world audio classification.</p>

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.

AN OVERVIEW OF AUDIO CLASSIFIER METHODS AND TECHNIQUES

A brief overview of trends and developments in the area of Content-Based Audio Indexing and Retrieval (CBAIR), during the past few years. Here we explored some limitations and constrains of existing Query by Example (QBE) and Query by Humming (QBH) CBAIR systems. We talked about different methods to represent musical objects, like feature-based representation, musical parameter-based representation; similarly retrieval strategies, like feature based retrieval as well as melody or theme based retrieval of musical objects, in this paper. Moreover, some important issues regarding to indexing and retrieval performance i.e. efficient indexing and retrieval complexity, in this area are discussed thoroughly. Finally, hypothetical solutions for the existing problems in this area are proposed to improve the performance.