General notes

Before going into the details of the possible projects it is worth pointing out some general tasks that will be required no matter what the specific project is.

Music/Speech Discrimination

Separation of music from speech is useful in automatic speech recognition applications. It can also be used to identify between songs and advertisements in radio broadcasts. It can also be useful in audio databases where it is desirable to have content-based indices that will allow a user to query specific types of data. The characteristics of music and speech are different as they have different sources, hence by the appropriate choice of features we should be able to distinguish between music and speech. The references list a variety of features that can be used to identify the music and speech segments. Emphasis in this project should be on the use of wavelet features to do the above.
Segments of speech containing both music and speech will be provided, the goal is to segment the audio into separate portions containing music and speech.
References   1 2

Contour Representation

In applications like computer graphics, image analysis, it is required to describe curves to facilitate further processing. Approximations of curves can be obtained by using multiresolution analysis. The features used to represent the curve should be insensitive to rotation, translation and scaling. Features extracted by wavelet analysis satisfy these constraints and are suitable for contour representation.
In this project several contours will be provided and the goal will be use wavelets to efficiently represent them.
References   1

Texture Classification

Texture Classification involves classifying an unknown texture into one of several classes based on known textures. The classification is motivated by similarity between the unknown and known texture. While this classification is relatively easy to perform when done visually, it is heavily dependent on the features used for classification when done by a computer. In this project the objective is to classify the unknown texture(s). Wavelet based features should be extracted from the unknown texture and a suitable distance metric should be used to compare it with the features of the known texture.
Several textures will be provided as reference textures. Natural and synthetic textures will need to classified into one of the class of the reference texture.
References   1


Noise in images can be added due to camera, scanners etc. To improve the quality of the image it is desirable to remove/suppress the effect of noise. In this project images corrupted with additive noise will be given, the objective will be to use wavelets to suppress the noise in the images. In order to do denoising wavelet coefficients are extracted from the image and then noise suppression is done by hard/soft thresholding. The basic idea is that natural signals not corrupted by noise tend to be "smooth", whereas when noise is introduced the degree of smoothness is reduced (e.g., more coefficients appear at high frequencies than in a noise free image.) In order to obtain better performance, wavelet packets and adaptive techniques should be used.
References   1 2

Texture Generation

The texture generation problem can be seen as a converse of the texture classification problem. While some textures have some well defined structure, others are more noise like. The basic idea here is to provide a mechanism such that textures can be generated by generating random values for wavelet coefficients. By choosing the appropriate random representation and the different random models for each subband, different types of textures can be generated.


Wavelets have been very effective in representation and analysis of data in Euclidean spaces. However in applications like computer graphics, mapping earth topology, we need to deal with data on complex geometries(ex surface of a sphere). Traditional wavelets cannot be used for analysis of data on these complex geometries. Spherical wavelets(1) have been proposed which can be used for representation and compression of functions on spheres. In this project graphical data on a sphere will be given and spherical wavelets (or any other suitable wavelet) should be used for analysis of data defined on the sphere.
References   1 2


For better compression, image analysis, classification; segmentation of the image is important. Here the image is separated into different regions such that each region is relatively homogeneous. Wavelet based features provide a good representation of the image and can be used for image segmentation purposes. Several images(natural and synthetic) with varying textures will be given and the goal will be to efficiently segment the image.
References   1 2