Abstrait

Audio Type Identification Using EEMD: A Noise Assisted Data Analysis Method

Mr. Vinayak D. Chavan, Dr. Sanjay L. Nalbalwar

Audio classification is a process of assigning particular class to an audio signal. Classifying the audio signal has many applications in the field of digital library, automatic organization of databases etc. In the last several years efforts have been made to develop different methods to extract information from audio signals, so that they may be stored, organized and retrieved automatically whenever required. In this work, audio signals are classified into different categories based on spectral and temporal features. In this methodology, the audio signal is initially decomposed into overlapped frames. Ensemble Empirical Mode Decomposition (EEMD), which is noise assisted data analysis method, is used to convert these frames into a set of band-limited functions known as Intrinsic Mode Functions (IMFs). Temporal and Spectral features then extracted from these IMFs and thereafter classification is done using Gaussian Mixture Model (GMM) classifier. Different combinations of features were tested to create feature vector. The experimental results showed accuracy of more than 80%.