Name
Abstract | ||
|---|---|---|
In this paper, we propose a new strategy to obtain super-resolution maps of the sound field recorded by a spherical microphone array. In recent works, we have demonstrated that sparse recovery (SR) algorithms based on the minimisation of the l(p) norm with 0 < p <= 1 can effectively produce super-resolution acoustic maps. The issue with the minimisation of the l(p) norm when p < 1 is that it is a non-convex optimisation problem, thus it is likely that the algorithm converges to a local minimum. In this paper we show that we can improve the convergence of our SR acoustic imaging methods by providing, to the SR solver, priming information relating to the spatial location of the sound sources. This information can be acquired with a pre-processing, coarse analysis using standard blind source separation or direction-of-arrival techniques. Simulation results indicate that this approach can provide accurate estimates of the positions of multiple, simultaneous sound sources in the presence of noise or reverberation and even in an under-determined situation. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Acoustic imaging, Sparse recovery, Spherical microphone arrays |
Field | DocType | ISSN |
Convergence (routing),Reverberation,Pattern recognition,Computer science,Microphone array,Minimisation (psychology),Artificial intelligence,Norm (mathematics),Solver,Blind signal separation,Compressed sensing | Conference | 1520-6149 |
Citations | PageRank | References |
1 | 0.37 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tahereh Noohi | 1 | 8 | 1.60 |
| Nicolas Epain | 2 | 55 | 6.70 |
| Craig T. Jin | 3 | 16 | 2.60 |