Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Robotics and Autonomous Systems
A maximum-likelihood parametric approach to source localizations
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
The fusion of distributed microphone arrays for sound localization
EURASIP Journal on Applied Signal Processing
Performance of GCC-and AMDF-based time-delay estimation in practical reverberant environments
EURASIP Journal on Applied Signal Processing
Time delay estimation in room acoustic environments: an overview
EURASIP Journal on Applied Signal Processing
Particle filter with integrated voice activity detection for acoustic source tracking
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Generalized Steered Response Power Method for Computationally Viable Source Localization
IEEE Transactions on Audio, Speech, and Language Processing
Multiple source localization based on acoustic map de-emphasis
EURASIP Journal on Audio, Speech, and Music Processing
Accessible speech-based and multimodal media center interface for users with physical disabilities
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
Online blind speech separation using multiple acoustic speaker tracking and time-frequency masking
Computer Speech and Language
Hi-index | 0.00 |
The behavior of time delay estimation (TDE) is well understood and therefore attractive to apply in acoustic source localization (ASL). A time delay between microphones maps into a hyperbola. Furthermore, the likelihoods for different time delays are mapped into a set of weighted nonoverlapping hyperbolae in the spatial domain. Combining TDE functions from several microphone pairs results in a spatial likelihood function (SLF) which is a combination of sets of weighted hyperbolae. Traditionally, the maximum SLF point is considered as the source location but is corrupted by reverberation and noise. Particle filters utilize past source information to improve localization performance in such environments. However, uncertainty exists on how to combine the TDE functions. Results from simulated dialogues in various conditions favor TDE combination using intersection-based methods over union. The real-data dialogue results agree with the simulations, showing a 45% RMSE reduction when choosing the intersection over union of TDE functions.