Acoustic Scene Classification by using Combination of MODWPT and Spectral Features ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers.
by Mie Mie Oo | Lwin Lwin Oo “Acoustic Scene Classification by using Combination of MODWPT and Spectral Features”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,
Acoustic Scene Classification by using Combination of MODWPT and Spectral Features ijtsrd IEEE PAPER
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