Accurately recognizing speech is a difficult task. Differences in gender, accent, pace, tone, as well as defects in the recording equipment and environmental noise can disturb a voice signal. Speech recognition systems are commonly studied and implemented by companies trying to alleviate problems, such as illness or injury, or to increase overall efficiency. This research uses wavelet analysis with several traditional methods to study similarities among sound signals. Through a series of seven steps, a similarity analysis of some voice signals from the same speaker as well as from different speakers is performed. The efficiency of four different wavelets (Haar, db2, db4 and Discrete Morlet), different correlation methods developed previously or in this research, and two different Dynamic Time Warping methods are studied in this research. Through several experiments, it will be shown that these techniques produce excellent results for signals by the same speaker. Based on the limited number of cases studied in this research, some evidence will be presented that suggests the proposed methods on this research are more effective for recognizing male voice files than those of females.
In this paper lifting is used for similarity analysis and classification of sets of similar medical images. The lifting scheme is an invertible wavelet transform that maps integers to integers. Lifting provides efficient in-place calculation of transfer coefficients and is widely used for analysis of similar image sets. Images of a similar set show high degrees of correlation with one another. The inter-set redundancy can be exploited for the purposes of prediction, compression, feature extraction, and classification. This research intends to show that there is a higher degree of correlation between images of a similar set in the lifting domain than in the pixel domain. Such a high correlation will result in more accurate classification and prediction of images in a similar set. Several lifting schemes from Calderbank-Daubechies-Fauveue's family were used in this research. The research shows that some of these lifting schemes decorrelates the images of similar sets more effectively than others. The research presents the statistical analysis of the data in scatter plots and regression models.
A wavelet-lifting scheme that maps integers to integers, performs all calculations in-place, and is computationally efficient is used in this paper. It processes 2-D medical images row by row producing an equivalent 1-D signal. The interdependency of pixels in 2-D medical images is known to vary in different regions. Thus, some lifting schemes decorrelate the resultant signal more efficiently than others do. The effect of different scanning approaches on the performance of several lifting schemes is presented.
There is a great amount of similarity in a set of medical images. Set Redundancy Compression (SRC) has shown that compression of a similar set of images can provide better compression than the compression obtained from compressing the individual images of the set. SRC is based on the prediction of the other images in the set from a smaller subset (this subset can be as small as one image). This paper presents a new wavelet based prediction method for prediction of the intermediate images in a similar set of medical images. The technique uses the correlation between coefficients in the wavelet transforms of the image set to produce a better image prediction compared to direct image prediction.
A major problem associated with a `film-less hospital' is the amount of digital image data that is generated and stored. Image compression must be used to reduce the storage size. Most current image compression methods were developed for the compression of single images. A new compression that uses similar image set redundancy and minimal numbers of orthogonal features can be used to efficiently compress medical images. As presented in this paper, wavelet analysis, principal component analysis, and statistical recrimination can be successfully used to optimally denote image differences and achieve efficient similar set image compression for medical images.