Soumya Kanti Ghosh

Learn More
Fast growth of mobile sensing technologies, like GPS in smart phones, made capturing position data in the form of trajectories easy. But detecting anomalous trajectories, which are grossly different from remaining trajectories, is a major challenge in the surveillance domain and a big data problem. In the present work, a novel density-based method has been(More)
Most of the conventional speech enhancement methods operating in the spectral domain often suffer from spurious artifact called musical noise. Moreover, these methods also incur an extra overhead time for noise power spectral density estimation. In this paper, a speech enhancement framework is proposed by cascading two temporal processing stages. The first(More)
This paper presents a single-channel speech separation method implemented with a deep recurrent neural network (DRNN) using recurrent temporal restricted Boltzmann machines (RTRBM). Although deep neural network (DNN) based speech separation (denoising task) methods perform quite well compared to the conventional statistical model based speech enhancement(More)
This paper introduces a single-channel speech enhancement framework based on global soft-decision, where the magnitude spectrum of clean speech is estimated by combining two separate Bayesian estimators based on voiced-unvoiced uncertainty. For the voiced regions, the perceptually motivated adaptive β-order weighted minimum mean square error (MMSE)(More)
  • 1