462 research outputs found
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
A Geometric Approach to Sound Source Localization from Time-Delay Estimates
This paper addresses the problem of sound-source localization from time-delay
estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel
geometric formulation is proposed, together with a thorough algebraic analysis
and a global optimization solver. The proposed model is thoroughly described
and evaluated. The geometric analysis, stemming from the direct acoustic
propagation model, leads to necessary and sufficient conditions for a set of
time delays to correspond to a unique position in the source space. Such sets
of time delays are referred to as feasible sets. We formally prove that every
feasible set corresponds to exactly one position in the source space, whose
value can be recovered using a closed-form localization mapping. Therefore we
seek for the optimal feasible set of time delays given, as input, the received
microphone signals. This time delay estimation problem is naturally cast into a
programming task, constrained by the feasibility conditions derived from the
geometric analysis. A global branch-and-bound optimization technique is
proposed to solve the problem at hand, hence estimating the best set of
feasible time delays and, subsequently, localizing the sound source. Extensive
experiments with both simulated and real data are reported; we compare our
methodology to four state-of-the-art techniques. This comparison clearly shows
that the proposed method combined with the branch-and-bound algorithm
outperforms existing methods. These in-depth geometric understanding, practical
algorithms, and encouraging results, open several opportunities for future
work.Comment: 13 pages, 2 figures, 3 table, journa
Room geometry blind inference based on the localization of real sound source and first order reflections
The conventional room geometry blind inference techniques with acoustic
signals are conducted based on the prior knowledge of the environment, such as
the room impulse response (RIR) or the sound source position, which will limit
its application under unknown scenarios. To solve this problem, we have
proposed a room geometry reconstruction method in this paper by using the
geometric relation between the direct signal and first-order reflections. In
addition to the information of the compact microphone array itself, this method
does not need any precognition of the environmental parameters. Besides, the
learning-based DNN models are designed and used to improve the accuracy and
integrity of the localization results of the direct source and first-order
reflections. The direction of arrival (DOA) and time difference of arrival
(TDOA) information of the direct and reflected signals are firstly estimated
using the proposed DCNN and TD-CNN models, which have higher sensitivity and
accuracy than the conventional methods. Then the position of the sound source
is inferred by integrating the DOA, TDOA and array height using the proposed
DNN model. After that, the positions of image sources and corresponding
boundaries are derived based on the geometric relation. Experimental results of
both simulations and real measurements verify the effectiveness and accuracy of
the proposed techniques compared with the conventional methods under different
reverberant environments
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