A Novel Three-Stage Frequency Offset Estimator for OFDM
A novel frequency offset estimate method is proposed for OFDM systems. In the proposed method, a special preamble composed of two training symbol blocks with several wide-sense identical parts is designed. To obtain a large estimate range together with a good estimation performance, a novel three-stage CFO estimate scheme based on the designed preamble is proposed. In this scheme, initial CFO estimates are first obtained over the two training symbol blocks by means of a novel two-stage estimate method. In the third stage, the two initial CFO estimates are jointly used to enlarge the CFO estimate range. Simulation results demonstrate that the novel three-stage frequency offset estimator has a satisfying estimate performance while achieving a large estimate range and maintaining a low computational complexity
This paper presents a simple, but efficient and robust, method for music genre classification that utilizes sparse representations in overcomplete dictionaries. The training step involves creating dictionaries, using the K-SVD algorithm, in which data corresponding to a particular music genre has a sparse representation. In the classification step, the Orthogonal Matching Pursuit (OMP) algorithm is used to separate feature vectors that consist only of Linear Predictive Coding (LPC) coefficients. The paper analyses in detail a popular case study from the literature, the ISMIR 2004 database. Using the presented method, the correct classification percentage of the 6 music genres is 85.59, result that is comparable with the best results published so far.