Guo Guodong, Li S Z
Comput. Sci. Dept., Univ. of Wisconsin, Madison, WI, USA.
IEEE Trans Neural Netw. 2003;14(1):209-15. doi: 10.1109/TNN.2002.806626.
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.
支持向量机（SVM）最近被提出作为一种用于模式识别的新学习算法。在本文中，采用具有二叉树识别策略的支持向量机来解决音频分类问题。我们在一个包含16类409种声音的通用音频数据库上展示了支持向量机的潜力。我们将基于支持向量机的分类与其他流行方法进行比较。对于音频检索，我们提出了一种新的度量标准，称为边界距离（DFB）。当给出一个查询音频时，系统首先找到查询模式所在的边界。然后，数据库中的所有音频模式按它们到该边界的距离进行排序。所有边界均由支持向量机学习并与音频数据库一起存储。给出了音频检索的实验比较，以表明这种新度量标准相对于其他相似性度量的优越性。
