(IPSN 2020) 2020 International Conference on Information Processing in Sensor Networks
April 21-24, 2020
Location: Changed to--Virtual Conference
April 21-24, 2020
Location: Changed to--Virtual Conference
December 11-12, 2019
Location: Brussels, Belgium
Audience: Fresh PhD who want to apply their ML skill to develop innovative applications in audio and music, with direct implementation within a commercialized product, and an ambitious technological roadmap for the years to come.
Job description: full time job within a team of 4 engineer / researchers / developers, completely integrated with the 12 members of the company.
Summary:
Biamp Systems is seeking an intelligent and motivated DSP Engineer that is passionate about digital audio theory and algorithm implementation. In this role, the DSP Engineer designs, develops, maintains, and tests DSP audio algorithms and applications used to provide novel solutions in teleconferencing, video conferencing, and sound reinforcement systems. The DSP Engineer may also contribute to audio centric machine learning projects, designing and creating data sets and managing machine learning infrastructure systems.
The IEEE Open Journal of Signal Processing covers the enabling technology for the generation, transformation, extraction, and interpretation of information. It comprises the theory, algorithms with associated architectures and implementations, and applications related to processing information contained in many different formats broadly designated as signals.
Lecture Date: March 19-21, 2020
Chapter: Chennai
Chapter Chair: S. Salivahanan
Topic: Audio-Visual Voice Activity Detection Using Deep Neural Networks,
Array processing and beamforming with Kronecker products
Lecture Date: March 16-17, 2020
Chapter: Bangalore
Chapter Chair: Venkatesh Radhakrishnan
Topic: Optimal Multichannel signal enhancement, Audio-Visual
Voice Activity Detection Using Deep Neural Networks
Lecture Date: March 12-13, 2020
Chapter: Pune
Chapter Chair: Anil S. Tavildar
Topic: Array processing and beamforming with Kronecker products,
Audio-Visual Voice Activity Detection Using Deep Neural Networks
The problem of detecting a high-dimensional signal based on compressive measurements in the presence of an eavesdropper (Eve) is studied in this paper. We assume that a large number of sensors collaborate to detect the presence of sparse signals while the Eve has access to all the information transmitted by the sensors to the fusion center (FC).
The topic of sequence design has received considerable attention due to its wide applications in active sensing. One important desired property for the design sequence is the spectral shape. In this paper, the sequence design problem is formulated by minimizing the regularized spectral level ratio subject to a peak-to-average power ratio constraint.