tailieunhanh - Mpeg 7 audio and beyond audio content indexing and retrieval phần 4

Trong Chương 3 người đọc được giới thiệu với các khái niệm về tương tự như âm thanh và phân loại âm thanh. Phân loại khác nhau và các tài sản của họ được thảo luận. Mô tả Lowlevel được giới thiệu trong chương trước được sử dụng để minh họa. Các tiêu chuẩn MPEG-7 là một lần nữa được sử dụng như một | MPEG-7 SOUND CLASSIFICATION 73 where O x is a mapping from the input space to a possibly infinite dimensional space. There are three kernel functions for the nonlinear mapping 1. Polynomial K x y fxy 1 z where parameter z is the degree of the polynomial. 2. Gaussian radial basis functions K x y exp - x - y 2 2o-2 where the parameter Ơ is the standard deviation of the Gaussian function. 3. MLP function K x y tanh scale xy - offset where scale and offset are two given parameters. SVMs are classifiers for multi-dimensional data that essentially determine a boundary curve between two classes. The boundary can be determined only with vectors in boundary regions called the margin of two classes in a training data set. SVMs therefore need to be relearned only when vectors in boundaries change. From the training examples SVM finds the parameters of the decision function which can classify two classes and maximize the margin during a learning phase. After learning the classification of unknown patterns is predicted. SVMs have the following advantages and drawbacks. Advantages The solution is unique. The boundary can be determined only by its support vectors. An SVM is robust against changes of all vectors but its support vectors. SVM is insensitive to small changes of the parameters. Different SVM classifiers constructed using different kernels polynomial radial basis function RBF neural net extract the same support vectors. When compared with other algorithms SVMs often provide improved performance. Disadvantages Very slow training procedure. MPEG-7 SOUND CLASSIFICATION The MPEG-7 standard Casey 2001 Manjunath etal. 2001 has adopted a generalized sound recognition framework in which dimension-reduced decorrelated log-spectral features called the audio spectrum projection ASP are used to train HMM for classification of various sounds such as speech explosions laughter trumpet cello etc. The feature extraction of the MPEG-7 sound recognition framework is based on the