Data-driven audio feature selection for audio quality recognition in broadcast news

In¹ this paper, we describe automatic audio quality recognition architecture for radio broadcast news based on audio feature selection, using the discrimination ability of the audio descriptors as a criterion of selection. Specifically, we labeled streams of broadcast news transmissions according to their audio quality based on the human auditory perception. Parameterization algorithms extract a large set of audio descriptors and an algorithm of data-driven criteria rank the descriptors’ relevance. After that, the feature subsets fed machine learning algorithms for classification. This methodology showed that the k-nearest neighbor classifier provides significantly good results, considering the achieved accuracy. Moreover, the experimental framework verifies the assumption that discarding irrelevant audio descriptors before the classification stage works in favor to the overall identification performance.