In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, highlevel feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting

Abstract—Cochlear implant patients often complain about their difficulty in understanding speech in noisy environments. Currently a fixed noise suppression algorithm is used in cochlear implants regardless of the characteristics of the speech or noise environment. Access to an intelligent mechanism to determine the noise environment on-the-fly in order to automatically switch between different noise suppression algorithms in real-time can enhance patients experience with cochlear implants. In this paper, we report the first prototype system implementing such a real-time switching mechanism for automatic selection between two noise suppression algorithms designed for two commonly encountered noisy environments. The results obtained indicate the feasibility of this on-the-fly switching for actual deployment in cochlear implants. C I.

c t i v it y e p o r t 2008 Table of contents

c t i v it y e p o r t 2007 Table of contents