论文标题
快速搜索类似音频项目的解决方案
Rapid solution for searching similar audio items
论文作者
论文摘要
寻找类似音频项的一种幼稚方法是将测试示例特征向量的每个条目与k-neartival邻居时尚的每个特征向量进行比较。这种方法已经有两个问题：音频信号由高维矢量表示，候选人的数量可能很大 - 想到成千上万。搜索过程将具有很高的复杂性。我们的论文将通过更具体地说，可以通过更具体地说的位置敏感的哈希来处理这个问题。该项目将以分类和聚类问题的精神。计算机声音生产原理将用于确定描述音频信号的功能是最有用的。这将在特征向量的大小下进行样本，并随后加速过程。
A naive approach for finding similar audio items would be to compare each entry from the feature vector of the test example with each feature vector of the candidates in a k-nearest neighbors fashion. There are already two problems with this approach: audio signals are represented by high dimensional vectors and the number of candidates can be very large - think thousands. The search process would have a high complexity. Our paper will treat this problem through hashing methodologies more specifically the Locality Sensitive Hashing. This project will be in the spirit of classification and clustering problems. The computer sound production principles will be used to determine which features that describe an audio signal are the most useful. That will down-sample the size of the feature vectors and speed up the process subsequently.
