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EnglishWe present the weighted minimum variance distortionless response (WMVDR), which is a steered response power (SRP) algorithm, for near-field speaker localization in a reverberant environment. The proposed WMVDR is based on a machine learning approach for computing the incoherent frequency fusion of narrowband power maps. We adopt a radial basis function network (RBFN) classifier for the estimation of the weighting coefficients, and a marginal distribution of narrowband power map as feature for the supervised training operation. Simulations demonstrate the effectiveness of the proposed approach in different conditions
| Titolo: | Frequency map selection using a RBFN-based classifier in the MVDR beamformer for speaker localization in reverberant rooms |
| Autori: | |
| Data di pubblicazione: | 2015 |
| Serie: | |
| Abstract: | We present the weighted minimum variance distortionless response (WMVDR), which is a steered response power (SRP) algorithm, for near-field speaker localization in a reverberant environment. The proposed WMVDR is based on a machine learning approach for computing the incoherent frequency fusion of narrowband power maps. We adopt a radial basis function network (RBFN) classifier for the estimation of the weighting coefficients, and a marginal distribution of narrowband power map as feature for the supervised training operation. Simulations demonstrate the effectiveness of the proposed approach in different conditions |
| Handle: | http://hdl.handle.net/11390/1103912 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| FreqMapSelection_finalProceedings.pdf | Articolo principale | Documento in Post-print | Non pubblico | Accesso ristretto Richiedi una copia |
http://hdl.handle.net/11390/1103912
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