@misc{9227685,
  abstract     = {{Reconstructing the geometric structure of a room from indoor acoustic signals (such as room impulse response, RIR) has significant application value in fields such as spatial audio, active sound field control, and virtual reality. However, in real physical measurement environments, inevitable acoustic environmental noise and spatial uncertainty in the placement of equipment (such as minor perturbations in the positions of microphones and sound sources) make deterministic inference methods based on traditional time-domain waveform matching face serious model mismatch and multi-peak posterior distributions. To address this challenge, this thesis proposes a Bayesian robust inference framework for room geometry under acoustic and spatial uncertainties. 
Firstly, this thesis thoroughly analyzes the vulnerability of the traditional waveform residual likelihood function when dealing with geometric perturbations, and proposes a likelihood construction method based on early reflection energy binning (Energy-Bin). This method effectively absorbs the time offset error at the sub-sampling level and reduces the model’s sensitivity to high-frequency phase mismatch, thereby significantly enhancing the robustness of the inference. Secondly, in response to the problem of excessively high computational cost of forward acoustic rendering in Markov Chain Monte Carlo (MCMC) posterior sampling, this thesis explores a highly optimized fast mirror source method (Fast ISM), which achieves an order of magnitude acceleration in computation while maintaining the accuracy of macroscopic acoustic energy characteristics. 
Furthermore, in order to overcome the complex local modes and multi-peak posterior structures in the joint posterior distribution of multiple microphones, this thesis introduces the multi-start simulated annealing Markov Chain Monte Carlo (SAMH) joint sampling strategy, effectively avoiding the local deadlock of the sampling chain. Finally, this thesis extends the inference model from the simple 3D estimation of room size to the 6D joint Bayesian inference of room geometry and sound source position, and conducts a systematic robustness test for the random perturbation of misspecified sensor positions. 
Simulations and experimental results show that, even under complex physical conditions with low signal-to-noise ratios and centimeter-level device-position perturbations, the Bayesian inference framework proposed in this thesis can recover room geometric parameters with improved stability, and can further estimate the source position under the tested 6D settings. It demonstrates significant advantages in system robustness and computational efficiency}},
  author       = {{Wang, Xiaohan and Gu, Junjie}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{BAYESIAN INFERENCE OF ROOM GEOMETRY FROM ROOM IMPULSE RESPONSES UNDER ACOUSTIC AND SPATIAL UNCERTAINTIES}},
  year         = {{2026}},
}