Raunaq Vohra

Kratarth Goel, Raunaq Vohra, Ainesh Bakshi

This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to handwritten digit recognition and text categorization.

Kratarth Goel, Raunaq Vohra, Anant Kamath et al.

In this paper, we present a novel brain computer interface based home automation system using two responses - Steady State Visually Evoked Potential (SSVEP) and the eye-blink artifact, which is augmented by a Bluetooth based indoor localization system, to greatly increase the number of controllable devices. The hardware implementation of this system to control a table lamp and table fan using brain signals has also been discussed and state-of-the-art results have been achieved.

Kratarth Goel, Raunaq Vohra, J. K. Sahoo

In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than an RBM. We apply this technique to the task of polyphonic music generation.

Kratarth Goel, Raunaq Vohra

Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is also not uncommon. However, these architectures do not always adequately consider the temporal dependencies in data. We thus propose a new generic architecture called the Deep Belief Network - Bidirectional Long Short-Term Memory (DBN-BLSTM) network that models sequences by keeping track of the temporal information while enabling deep representations in the data. We demonstrate this new architecture by applying it to the task of music generation and obtain state-of-the-art results.