Name
Title | ||
|---|---|---|
Particle filter design using importance sampling for acoustic source localisation and tracking in reverberant environments |
Abstract | ||
|---|---|---|
Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1155/ASP/2006/17021 | EURASIP Journal on Applied Signal Processing |
Keywords | Field | DocType |
acoustic source localisation,importance sampling,average tracking accuracy,basic bootstrap particle filter,bootstrap algorithm,new algorithm,new particle filter,proposed particle filter,tracking ability,real audio recording,particle filter design,reverberant environment | Computer vision,Importance sampling,Monte Carlo method,Reverberation,Computer science,Particle filter,Sensor array,Artificial intelligence,Microphone,Acoustic source localization,Filter design | Journal |
Volume | Issue | ISSN |
2006, | 1 | 1687-6180 |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric A. Lehmannand | 1 | 1 | 0.35 |
| Robert C. Williamson | 2 | 4191 | 755.22 |