TY - GEN
T1 - Exploiting structures of temporal causality for robust speaker localization in reverberant environments
AU - Schymura, Christopher
AU - Guo, Peng
AU - Maymon, Yanir
AU - Rafaely, Boaz
AU - Kolossa, Dorothea
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper introduces a framework for robust speaker localization in reverberant environments based on a causal analysis of the temporal relationship between direct sound and corresponding reflections. It extends previously proposed localization approaches for spherical microphone arrays based on a direct-path dominance test. So far, these methods are applied in the time-frequency domain without considering the temporal context of direction-of-arrival measurements. In this work, a causal analysis of the temporal structure of subsequent directions-of-arrival estimates based on the Granger causality test is proposed. The cause-effect relationship between estimated directions is modeled via a causal graph, which is used to distinguish the direction of the direct sound from corresponding reflections. An experimental evaluation in simulated acoustic environments shows that the proposed approach yields an improvement in localization performance especially in highly reverberant conditions.
AB - This paper introduces a framework for robust speaker localization in reverberant environments based on a causal analysis of the temporal relationship between direct sound and corresponding reflections. It extends previously proposed localization approaches for spherical microphone arrays based on a direct-path dominance test. So far, these methods are applied in the time-frequency domain without considering the temporal context of direction-of-arrival measurements. In this work, a causal analysis of the temporal structure of subsequent directions-of-arrival estimates based on the Granger causality test is proposed. The cause-effect relationship between estimated directions is modeled via a causal graph, which is used to distinguish the direction of the direct sound from corresponding reflections. An experimental evaluation in simulated acoustic environments shows that the proposed approach yields an improvement in localization performance especially in highly reverberant conditions.
KW - Multivariate granger causality test
KW - Speaker localization
KW - Spherical microphone arrays
KW - Vector autoregressive models
UR - http://www.scopus.com/inward/record.url?scp=85048558016&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93764-9_22
DO - 10.1007/978-3-319-93764-9_22
M3 - Conference contribution
AN - SCOPUS:85048558016
SN - 9783319937632
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 237
BT - Latent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Proceedings
A2 - Gannot, Sharon
A2 - Deville, Yannick
A2 - Mason, Russell
A2 - Plumbley, Mark D.
A2 - Ward, Dominic
PB - Springer Verlag
T2 - 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018
Y2 - 2 July 2018 through 5 July 2018
ER -