Smartphone-based real-time classification of noise signals using subband features and random forest classifier

Fatemeh Saki; Abhishek Sehgal; Issa M. S. Panahi; Nasser Kehtarnavaz

ICASSP 2016accepted0 citations

Smartphone-based real-time classification of noise signals using subband features and random forest classifier

Fatemeh Saki, Abhishek Sehgal, Issa M. S. Panahi, Nasser Kehtarnavaz

Abstract

This paper presents the real-time implementation and field testing of an app running on smartphones for classifying noise signals involving subband features and a random forest classifier. This app is compared to a previously developed app utilizing mel-frequency cepstral coefficients features and a Gaussian mixture model classifier. The real-time implementation has been carried out on both the Android and iOS smartphones. The field testing results indicate the superiority of this newly developed app over the previously developed app in terms of classification rates.

BibTeX

@inproceedings{icassp2016_smartphonebasedr,
  title = {Smartphone-based real-time classification of noise signals using subband features and random forest classifier},
  author = {Fatemeh Saki and Abhishek Sehgal and Issa M. S. Panahi and Nasser Kehtarnavaz},
  booktitle = {ICASSP 2016},
  year = {2016}
}