1,307 research outputs found
Reflection-Aware Sound Source Localization
We present a novel, reflection-aware method for 3D sound localization in
indoor environments. Unlike prior approaches, which are mainly based on
continuous sound signals from a stationary source, our formulation is designed
to localize the position instantaneously from signals within a single frame. We
consider direct sound and indirect sound signals that reach the microphones
after reflecting off surfaces such as ceilings or walls. We then generate and
trace direct and reflected acoustic paths using inverse acoustic ray tracing
and utilize these paths with Monte Carlo localization to estimate a 3D sound
source position. We have implemented our method on a robot with a cube-shaped
microphone array and tested it against different settings with continuous and
intermittent sound signals with a stationary or a mobile source. Across
different settings, our approach can localize the sound with an average
distance error of 0.8m tested in a room of 7m by 7m area with 3m height,
including a mobile and non-line-of-sight sound source. We also reveal that the
modeling of indirect rays increases the localization accuracy by 40% compared
to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at
(https://youtu.be/TkQ36lMEC-M
Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework
The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-maximization (EM). In this paper we propose a new mixture
model that associates a weight with each observed point. We introduce the
weighted-data Gaussian mixture and we derive two EM algorithms. The first one
considers a fixed weight for each observation. The second one treats each
weight as a random variable following a gamma distribution. We propose a model
selection method based on a minimum message length criterion, provide a weight
initialization strategy, and validate the proposed algorithms by comparing them
with several state of the art parametric and non-parametric clustering
techniques. We also demonstrate the effectiveness and robustness of the
proposed clustering technique in the presence of heterogeneous data, namely
audio-visual scene analysis.Comment: 14 pages, 4 figures, 4 table
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