Motivation: While many company’s are expending great effort in the field of automatic speech recognition(ASR), little attention is being paid to general audio and long-term modeling of audio in general. Even an ASR system which could give a complete transcription of the words heard in an environment would lack vital information. e.g., who was talking, when they were talking, what was the tone o...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Segmenting audio streams in a signi cant manner and clustering sound segments objectively, is a signi cant challenge due to the nature of audio data. This paper presents some preliminary work on clustering sound segments based on frequency and harmonic characteristics. New metrics for comparing the similarity of sound segments are also devised.

This paper deals with the description of the use of a collaborative multi-strategy classification applied to image analysis. This system integrates different kinds of unsupervised classification methods and produce for each classifier a result built according to the results of all the other methods. Each method occurrence tries to make its result converge towards the results of the other method...