Feature Analysis for Audio Classification

GASTON BENGOLEA; DANIEL ACEVEDO; MARTIN RAIS; MARTA MEJAIL

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (19th Iberoamerican Congress on Pattern Recognition)

Spriger-Verlag

Lugar: Heidelberg; Año: 2014; p. 239 - 246

In this work we analyze and implement several audio features. We emphasize our analysis on the ZCR feature and propose a modification making it more robust when signals are near zero. They are all used to discriminate the following audio classes: music, speech, environmental sound. An SVM classifier is used as a classification tool, which has proven to be efficient for audio classification. By means of a selection heuristic we draw conclusions of how they may be combined for fast classification.