The Audio Analysis Lab is organizing a Ph.D. course in August on Advanced Topics in Acoustic Array Signal Processing. The course will be given by Prof. Sharon Gannot from Bar-Ilan University, Israel, who is Guest Professor at Audio Analysis Lab and lab member Assistant Prof. Jesper Rindom Jensen. The course will be held August 13-17, 20 2018 at Rendsburggade 14 at CREATE in Aalborg. Participants from other universities and companies are welcome to participate. You can read more about the course here.

Description: Acoustic arrays are becoming a ubiquitous technology in many places, including in consumer electronics and healthcare technology. Microphone arrays are now found in smartphones, laptops, TVs, etc., and loudspeaker arrays are emerging as a promising technology in home entertainment systems, car audio systems, public announcement systems. Moreover, as wireless communication capabilities are becoming widespread, audio devices can now form ad hoc networks and cooperate when solving signal processing problems, such as estimation and filtering. This offers many new possibilities but also poses many new challenges, as it requires that many difficult, technical problems must be solved. In the course, a general introduction to acoustic array signal processing will be given, including commonly used models and assumptions as well as classical methods for solving problems such as localization, beamforming and noise reduction. The remainder of the course is then devoted to recent advances in acoustic array signal processing and applications. These include advances within, for example, model-based localization and beamforming, sound zone control with loudspeaker arrays, multi-channel noise reduction in ad hoc microphone arrays, noise statistics estimation, speech intelligibility prediction, and speech enhancement in binaural hearing aids.

The course is dedicated to the following subjects:

• Fundamentals: Definitions, narrow-band signals, near-filed and far-field, array manifold vector. Beamforming, uniform linear array, directivity pattern. Performance criteria (beam-width, sidelobe level, directivity, white noise gain). Sensitivity. Sampling of continuous aperture. Wide-band signals and nested arrays.
• Space-time random processes: Snapshots, spatial correlation matrix, signal and noise subspaces.
• Optimal array processors: MVDR (Capon), MPDR, Maximum SNR, MMSE, LCMV.
• Sensitivity and robustness: Noise fields and multi-path and their influence on performance. Superdirective beamformer. Diagonal loading.
• Adaptive spatial filtering: Frost method, generalized sidelobe canceller (GSC).
• Parameter estimation (DoA): ML estimation, resolution, Cramér-Rao lower bound.
• Classical methods for localization: Classical methods (Bartlett), method based on eigen-decomposition: Pisarenko, MUSIC, ESPRIT. Resolution. MVDR estimation. Performance evaluation and comparison.
• Advances: Model-based processing and estimation, multi-channel noise reduction, ad hoc microphone arrays.
• Applications: Speech processing, hearing aids, wireless acoustic sensor networks, loudspeaker arrays.