Publication Type : Conference Paper
Publisher : International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)
Source : International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE) (2014)
ISBN : 9781479949816
Accession Number : 14665820
Keywords : Audio data, audio scene identification process, audio signal processing, background sound, Cepstrum, environmental sound recognition, Feature extraction, features extraction, Gaussian Mixture Model, Gaussian mixture modeling, Gaussian processes, MATLAB, Mel Frequency Cepstrum Co-efficient, Mel frequency cepstrum co-efficients, mixture models, neural nets, neural network classifier, Neural networks, Noise measurement, Probabilistic model, signal classification, telecommunication computing, Training
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Verified : Yes
Year : 2014
Abstract : Environmental sound recognition is an audio scene identification process in which a person's location is found by analyzing the background sound. This paper deals with the prototype modeling for environmental sound recognition. Sound recognition involves the collection of audio data, extraction of important features, clustering of similar features and their classification. The Mel frequency cepstrum co-efficients are extracted. These features are used for clustering by a Gaussian mixture model which is a probabilistic model. Neural Network classifier is used for classification of the features and to identify the environmental audio scene. The implementation is done with the help of MATLAB. Five major environmental sounds which include the sound of car, office, restaurant, street, subway are considered. This shows a better efficiency than the already existing method. The efficiency achieved in this method is 98.9%.
Cite this Research Publication : S. P. Mohanapriya, Sumesh, E. P., and Karthika, R., “Environmental sound recognition using Gaussian mixture model and neural network classifier”, in International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014.