本站所有资源均为高质量资源,各种姿势下载。
In the context of the document: Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence. This algorithm offers a solution that eliminates the need for multi-dimensional integration by utilizing a training sequence. The LBG algorithm is of the iterative type, meaning that it goes through multiple iterations to achieve its goal. In each iteration, a large set of vectors, known as the training set, is processed. Typically, this training set, denoted as T={x1, x2, ..., xM}, consists of sampled vectors from a group of typical signals that will be encoded together. Here, xi represents a sampled training vector, and M represents the size of the training set, which is significantly larger than the codebook size N.