Environmental acoustic transformation and feature extraction for machine hearing

International Conference on Information Technology and Digital Applications 2018

计算机科学;无线电电子学

Catanghal, R.^1 ; Palaoag, T.^2 ; Dayagdag, C.^3

University of Antique, Sibalom, Antique, Philippines^1

University of the Cordilleras, Baguio, Philippines^2

Romblon State University, Odiongan, Romblon, Philippines^3

关键词: Convolutional neural network;    Environmental acoustics;    Environmental recognition;    Environmental sound classifications;    Environmental sounds;    Hearing system;    Sound recognition;    Spectral coefficients;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/482/1/012007/pdf
DOI  :  10.1088/1757-899X/482/1/012007

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper explores the transformation of environmental sound waveform and feature set into a parametric type representation to be used in analysis, recognition, and identification for auditory analysis of machine hearing systems. Generally, the focus of the research and study in sound recognition is concentrated on the music and speech domains, on the other hand, there are limited in non-speech environmental recognition. We analyzed and evaluated the different current feature algorithms and methods explored for the acoustic recognition of environmental sounds, the Mel Filterbank Energies (FBEs) and Gammatone spectral coefficients (GSTC) and for classifying the sound signal the Convolutional Neural Network (CNN) was used. The result shows that GSTC performs well as a feature compared to FBEs, but FBEs tend to perform better when combined with other feature. This shows that a combination of features set is promising in obtaining a higher accuracy compared to a single feature in environmental sound classification, that is helpful in the development of the machine hearing systems.