International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Speaker identification under background noise using features extracted from steady vowel regions

Abstract: SUMMARY In this paper, we are exploring features extracted from steady vowel segments for improving the performance of speaker identification system under background noise. Steady vowel regions are produced by periodic impulse-like excitation and they contain relatively high signal energy. Hence, speaker specific information present in steady vowel regions may be less affected by the noise. In this work, steady vowel regions are determined by using the knowledge of accurate vowel onset points and epochs. Speaker identification studies are carried out using TIMIT database for white and vehicle noises. Universal background model–Gaussian mixture model-based modeling is explored for developing speaker models. Significant improvement in the performance of speaker identification is observed by using features extracted from steady vowel region in presence of noisy environments. Copyright © 2012 John Wiley & Sons, Ltd.

Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation

Abstract: Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

Camouflaged Object Detection and Tracking: A Survey

Abstract: Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, a...

A Multi-Space Approach to Zero-Shot Object Detection

Abstract: Object detection has been at the forefront for higher level vision tasks such as scene understanding and contextual reasoning. Therefore, solving object detection for a large number of visual categories is paramount. Zero-Shot Object Detection (ZSD) – where training data is not available for some of the target classes – provides semantic scalability to object detection and reduces dependence on large amount of annotations, thus enabling a large number of applications in real-life scenarios. In this paper, we propose a novel multi-space approach to solve ZSD where we combine predictions obtained in two different search spaces. We learn the projection of visual features of proposals to the semantic embedding space and class labels in the semantic embedding space to visual space. We predict similarity scores in the individual spaces and combine them. We present promising results on two datasets, PASCAL VOC and MS COCO. We further discuss the problem of hubness and show that our approach alleviates hubness with a performance superior to previously proposed methods.