Sparce Methods and compressed Sensing in Acoustics
Event details
| Date | 25.08.2016 |
| Hour | 14:00 › 16:00 |
| Speaker | Helena Peic Tukuljac |
| Location | |
| Category | Conferences - Seminars |
EDIC Candidacy Exam
Exam President: Prof. Hervé Bourlard
Thesis Director: Prof. Pierre Vandergheynst
Thesis Co-director: Dr. Hervé Lissek
Co-examiner: Prof. Pascal Frossard
Background papers:
Grid-free compressive beam- forming, by A. Xenaki, P. Gerstoft. The Journal of the Acoustical Society of America.
Low Frequency Interpolation of Room Impulse Responses Using Compressed Sensing, by R. Mignot, et al. " in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22.
Hearing behind walls: Localizing sources in the room next door with cosparsity, by S. Kiti, et al. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Abstract
In this research proposal an introduction to compressed
sensing applied to problems in acoustics is given. Due
to different types of sparsity that live in the acoustic domain,
sparse methods are a crucial tool for handling acoustical high dimensional
data. The focus is on the sparse inverse problems
related to the reconstruction of sound pressure field in a room
from a small number of microphones randomly distributed.
A combination of knowledge in signal processing, acoustics,
optimization and algorithms is established as the framework for
problem solving in this domain. Overview of the current results
is given, as well as the ideas for further research directions.
Exam President: Prof. Hervé Bourlard
Thesis Director: Prof. Pierre Vandergheynst
Thesis Co-director: Dr. Hervé Lissek
Co-examiner: Prof. Pascal Frossard
Background papers:
Grid-free compressive beam- forming, by A. Xenaki, P. Gerstoft. The Journal of the Acoustical Society of America.
Low Frequency Interpolation of Room Impulse Responses Using Compressed Sensing, by R. Mignot, et al. " in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22.
Hearing behind walls: Localizing sources in the room next door with cosparsity, by S. Kiti, et al. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Abstract
In this research proposal an introduction to compressed
sensing applied to problems in acoustics is given. Due
to different types of sparsity that live in the acoustic domain,
sparse methods are a crucial tool for handling acoustical high dimensional
data. The focus is on the sparse inverse problems
related to the reconstruction of sound pressure field in a room
from a small number of microphones randomly distributed.
A combination of knowledge in signal processing, acoustics,
optimization and algorithms is established as the framework for
problem solving in this domain. Overview of the current results
is given, as well as the ideas for further research directions.
Practical information
- General public
- Free
Contact
- Cecilia Chapuis EDIC