On investigating efficient methodology for environmental sound recognition

5Citations
Citations of this article
16Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This paper presents a comparative study of various methods to identify the environmental sounds. We evaluate two methods for feature extraction: Mel Frequency Cepstral Coefficients (MFCC) which is well known for speaker identification, and Matching Pursuit (MP) with Gabor Dictionary which gives a time frequency representation employed for scene recognition. In the classification stage, we show a comparison among Support Vector Machines (SVM), Logistic Regression, and Backpropagation Artificial Neural Network (BP-ANN). Simulation results show that MFCC gives a higher recognition performance as compared with MP. Furthermore, by concatenating MFCC features with some feature of MP, e.g., scale, might also improve performance in some situations. We observe that SVM show the best performance among the classifiers, for clean as well noisy signals. © 2013 IEEE.

Cite

CITATION STYLE

APA

Ruiz-Martinez, C. A., Akhtar, M. T., Washizawa, Y., & Escamilla-Hernandez, E. (2013). On investigating efficient methodology for environmental sound recognition. In ISPACS 2013 - 2013 International Symposium on Intelligent Signal Processing and Communication Systems (pp. 210–214). https://doi.org/10.1109/ISPACS.2013.6704548

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free