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Digital Voice Systems, Inc. (DVSI) is seeking a qualified Speech Research & Development Engineer at our office in Westford, MA. This is a great opportunity to join our team of world class engineers in designing high quality voice compression technology that is implemented in hundreds of millions of telecommunication systems world-wide.
Lecture Date: May 25, 2021 -- Virtual Lecture
Chapter: Madras
Chapter Chair: S. Salivahanan
Topic: Making sense out of restless brain activity with computational neuroimaging
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Researchers in Speech, Text and Multimodal Machine Translation at DFKI Saarbrücken, Germany
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The MT group at ML at DFKI Saarbrücken is looking for
senior researchers/researchers/junior researchers
in speech, text and multimodal machine translation using deep learning.
June 4-5, 2021
Application submission deadline: May 10, 2021
Location: Virtual conference
Workshop Flyer
The Signal Processing (SP) research group at the Universität Hamburg in Germany is hiring a Postdoc (E13/E14) "Machine Learning for Speech and Audio Processing".
October 6-8, 2021
Location: Tampere, Finland
Wide-sense cyclostationary processes are an important class of non-stationary processes that have a periodic structure in their first- and second-order moments. This article extends the notion of cyclostationarity (in the wide sense) to processes where the mean and covariance functions might depart from strict periodicities and constant amplitudes.
In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints.
Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target.
Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, the structures of signals that are highlighted by the representations are garbled when approaching the boundaries of the TF domain. In this paper, for the purpose of real-time TF information acquisition of nonstationary oscillatory time series, we propose a numerically efficient approach for the reduction of such boundary effects.