Discriminating Two Types of Noise Sources using Cortical Representation and Dimension Reduction Technique
Content-based audio classification techniques have focused on classifying events that are both semantically and perceptually distinct (such as speech, music, environmental sounds etc.). However, it is both useful and challenging to develop systems that can also discern sources that are semantically and perceptually close. In this paper we present results of our experiments on discriminating two types of noise sources. Particularly, we focus on machine-generated versus natural noise sources. A bio-inspired tensor representation of audio that models the processing at the primary auditory cortex is used for feature extraction. To handle large tensor feature sets, we use a generalized discriminant analysis method to reduce the dimension. We also present a novel technique of partitioning data into smaller subsets and combining the results of individual analysis before training pattern classifiers. The results of the classification experiments indicate that cortical representation performs 25% better than the common perceptual feature set used in audio classification systems (MFCCs).