Acoustic Sound Source Localisation and Tracking - in Indoor Environments
|Title:||Acoustic Sound Source Localisation and Tracking - in Indoor Environments|
|Series:||Blekinge Institute of Technology Doctoral Dissertation Series|
|Publisher:||Blekinge Institute of Technology|
|Organization:||Blekinge Institute of Technology|
|Department:||School of Engineering - Dept. of Signal Processing (Sektionen för teknik – avd. för signalbehandling)
School of Engineering S- 372 25 Ronneby
+46 455 38 50 00
|Abstract:||With advances in micro-electronic complexity and fabrication, sophisticated algorithms for source localisation and tracking can now be deployed in cost sensitive appliances for both consumer and commercial markets. As a result, such algorithms are becoming ubiquitous elements of contemporary communication, robotics and surveillance systems. Two of the main requirements of acoustic localisation and tracking algorithms are robustness to acoustic disturbances (to maximise localisation accuracy), and low computational complexity (to minimise power-dissipation and cost of hardware components).
The research presented in this thesis covers both advances in robustness and in computational complexity for acoustic source localisation and tracking algorithms.
This thesis also presents advances in modelling of sound propagation in indoor environments; a key to the development and evaluation of acoustic localisation and tracking algorithms.
As an advance in the field of tracking, this thesis also presents a new method for tracking human speakers in which the problem of the discontinuous nature of human speech is addressed using a new state-space filter based algorithm which incorporates a voice activity detector. The algorithm is shown to achieve superior tracking performance compared to traditional approaches. Furthermore, the algorithm is implemented in a real-time system using a method which yields a low computational complexity.
Additionally, a new method is presented for optimising the parameters for the dynamics model used in a state-space filter. The method features an evolution strategy optimisation algorithm to identify the optimum dynamics’ model parameters.
Results show that the algorithm is capable of real-time online identification of optimum parameters for different types of dynamics models without access to ground-truth data.
Finally, two new localisation algorithms are developed and compared to older well established methods. In this context an analytic analysis of noise and room reverberation is conducted, considering its influence on the performance of localisation algorithms. The algorithms are implemented in a real-time system and are evaluated with respect to robustness and computational complexity. Results show that the new algorithms outperform their older counterparts, both with regards to computational complexity, and robustness to reverberation and background noise.
The field of acoustic modelling is advanced in a new method for predicting the energy decay in impulse responses simulated using the image source method.
The new method is applied to the problem of designing synthetic rooms with a defined reverberation time, and is compared to several well established methods for reverberation time prediction. This comparison reveals that the new method is the most accurate.
Signal Processing\Detection and Classification
|Keywords:||Room acoustics, Speaker localisation, Tracking, State-space filter, Sequential Monte Carlo method, Particle filter|