A Physiologically Inspired Method for Audio Classification

被引：0

作者：

Sourabh Ravindran

Kristopher Schlemmer

David V. Anderson

机构：

[1] Georgia Institute of Technology,School of Electrical and Computer Engineering, College of Engineering

来源：

EURASIP Journal on Advances in Signal Processing | / 2005卷

关键词：

auditory model; feature extraction; neural nets; audio classification; Gaussian mixture models;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

We explore the use of physiologically inspired auditory features with both physiologically motivated and statistical audio classification methods. We use features derived from a biophysically defensible model of the early auditory system for audio classification using a neural network classifier. We also use a Gaussian-mixture-model (GMM)-based classifier for the purpose of comparison and show that the neural-network-based approach works better. Further, we use features from a more advanced model of the auditory system and show that the features extracted from this model of the primary auditory cortex perform better than the features from the early auditory stage. The features give good classification performance with only one-second data segments used for training and testing.

引用