Content-based audio classification and retrieval by support vector machines

Abstract

Support vector machines (SVMs) have been recently proposed as a new learning algorithm for pattern recognition. In this paper, the SVMs with a binary tree recognition strategy are used to tackle the audio classification problem. We illustrate the potential of SVMs on a common audio database, which consists of 409 sounds of 16 classes. We compare the SVMs based classification with other popular approaches. For audio retrieval, we propose a new metric, called distance-from-boundary (DFB). When a query audio is given, the system first finds a boundary inside which the query pattern is located. Then, all the audio patterns in the database are sorted by their distances to this boundary. All boundaries are learned by the SVMs and stored together with the audio database. Experimental comparisons for audio retrieval are presented to show the superiority of this novel metric to other similarity measures.