Skip to main content
placeholder image

Forming ad-hoc microphone arrays through clustering of acoustic room impulse responses

Conference Paper


Abstract


  • This paper investigates the formation of ad-hoc microphone arrays for the purpose of recording multiple sound sources by clustering microphones spatially distributed within a room. A novel codebook-based unsupervised method for cluster formation using features derived from the Room Impulse Responses (RIRs) corresponding to each microphone is proposed and compared with baseline clustering and classification methods. The features correspond to the sequence of arrival time and time delays of echoes as estimated by peaks of the RIRs along with peak amplitudes. Results suggest that the proposed codebook based clustering algorithm can outperform KNN supervised classification method and kmeans unsupervised clustering method applied to microphone segmentation and clustering, in terms of clustering success rate and noise robustness.

Authors


  •   Pasha, Shahab (external author)
  •   Zou, Yue-Xian (external author)
  •   Ritz, Christian H.

Publication Date


  • 2015

Citation


  • S. Pasha, Y. X. Zou & C. Ritz, "Forming ad-hoc microphone arrays through clustering of acoustic room impulse responses,"^^ in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, 2015, pp. 84-88.

Scopus Eid


  • 2-s2.0-84957555524

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5626

Start Page


  • 84

End Page


  • 88

Abstract


  • This paper investigates the formation of ad-hoc microphone arrays for the purpose of recording multiple sound sources by clustering microphones spatially distributed within a room. A novel codebook-based unsupervised method for cluster formation using features derived from the Room Impulse Responses (RIRs) corresponding to each microphone is proposed and compared with baseline clustering and classification methods. The features correspond to the sequence of arrival time and time delays of echoes as estimated by peaks of the RIRs along with peak amplitudes. Results suggest that the proposed codebook based clustering algorithm can outperform KNN supervised classification method and kmeans unsupervised clustering method applied to microphone segmentation and clustering, in terms of clustering success rate and noise robustness.

Authors


  •   Pasha, Shahab (external author)
  •   Zou, Yue-Xian (external author)
  •   Ritz, Christian H.

Publication Date


  • 2015

Citation


  • S. Pasha, Y. X. Zou & C. Ritz, "Forming ad-hoc microphone arrays through clustering of acoustic room impulse responses,"^^ in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, 2015, pp. 84-88.

Scopus Eid


  • 2-s2.0-84957555524

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5626

Start Page


  • 84

End Page


  • 88