The Signal Processing Laboratory at ICS-FORTH, aspires to be at the forefront of signal processing with fundamental work on image, audio, and speech signal processing and learning theory. Our research activities rely on the pillars of non-Gaussian statistics, sinusoidal modeling, sparse representations, and compressed sensing, and our aim is to apply state of the art techniques to a wide range of real world problems. Representative applications include imaging and video coding, distributed signal and data management over sensor networks, immersive audio and multichannel audio coding, speech recognition and enhancement.

Imaging

We synthesize, extend, and apply theoretical breakthroughs on Machine Learning, Compressed Sensing, and Sparse Representations, to emerging Imaging applications. Our key areas of interest range from Active Range Imaging, to Land-cover classification for remote sensing, and Computational Photography.

Read More

Distributed Sensor Networks

We have a distinct focus on signal acquisition/representation, distributed processing, and context retrieval from heterogeneous sensing devices, as well as on autonomic networking algorithms for limited bandwidth, limited power, and limited capacity wireless sensors, which serve the design paradigm of IoT and CPS.

Read More

Audio & Speech

We perform cutting edge research, featuring microphone array signal processing, wireless acoustic sensor networks, multichannel audio coding, immersive audio capturing and reproduction, sound source separation, speech enhancement for improved intelligibility, and robust automatic speech recognition.

Read More

Featured Publications

Tzagkarakis George , et. al, Signal and Data Processing Techniques for Industrial Cyber-Physical Systems, Rawat, Danda, Rodrigues, Joel, Stojmenovic, Ivan (Ed.), Cyber Physical Systems: From Theory to Practice, CRC Press, USA, pp 181-226, 2015.

Pavlidi Despoina, et. al, Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array, IEEE Transactions on Audio, Speech, and Language Processing, 21 (10), pp. 2193-2206, 2013, ISSN: 1558-7916.

Nikitaki Sofia, et. al, Efficient Multi-Channel Signal Strength based Localization via Matrix Completion and Bayesian Sparse Learning, Mobile Computing, IEEE Transactions on, 14 (11), pp. 2244-2256, 2015.

Griffin, Anthony, et. al, Localizing Multiple Audio Sources in a Wireless Acoustic Sensor Network, Signal Processing, Special Issue on Wireless Acoustic Sensor Networks and Ad-hoc Microphone Arrays, 107, pp. 54-67, 2015.