SPEECH ENHANCEMENT BASED ON SPARSE THEORY UNDER NOISY ENVIRONMENT

[[abstract]]Recently, the sparse algorithm for sparse enhancement is more and more popular issues. In this paper, we classify the process of the sparse theory to enhance speech signal into two parts, one is for dictionary training part and the other is signal reconstruction part. We focus on the White Gaussian Noise. Clean speech dictionary D is trained by K-SVD algorithm. The orthogonal matching pursuit(OMP) algorithm is used to obtain the sparse coefficients X of clean speech dictionary D. Denoising performance of the experiments shows that our proposed method is superior than other methods in SNR, LLR, SNRseg and PESQ.[[sponsorship]]National Taipei University[[conferencetype]]國際[[conferencedate]]20150718~20150719[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

3D FACE MODEL CONSTRUCTION BASED ON KINECT FOR FACE RECOGNITION

[[abstract]]We propose a simpler and faster method to recognize face. First, we use Kinect to detect frontal face and get depth image information with face, then we portrayed face in OpenGL to construct a three-dimensional face model based on the depth information. The face model also retains texture information of the original face images, and to create a complete change depth of face. It has a good result of repairing the distortion in side face. We can get a set face images with different angles by the method proposed, In recognition part, we use PCA(Principal Component Analysis) to reduce the dimensions, and classified with SVM(Support Vector Machine). The experiments show that the side face recognition can have good results.[[sponsorship]]National Taipei University[[conferencetype]]國際[[conferencedate]]20150718~20150719[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa