848 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
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Semi-supervised source localization in reverberant environments with deep generative modeling
We propose a semi-supervised approach to acoustic source localization in
reverberant environments based on deep generative modeling. Localization in
reverberant environments remains an open challenge. Even with large data
volumes, the number of labels available for supervised learning in reverberant
environments is usually small. We address this issue by performing
semi-supervised learning (SSL) with convolutional variational autoencoders
(VAEs) on reverberant speech signals recorded with microphone arrays. The VAE
is trained to generate the phase of relative transfer functions (RTFs) between
microphones, in parallel with a direction of arrival (DOA) classifier based on
RTF-phase. These models are trained using both labeled and unlabeled RTF-phase
sequences. In learning to perform these tasks, the VAE-SSL explicitly learns to
separate the physical causes of the RTF-phase (i.e., source location) from
distracting signal characteristics such as noise and speech activity. Relative
to existing semi-supervised localization methods in acoustics, VAE-SSL is
effectively an end-to-end processing approach which relies on minimal
preprocessing of RTF-phase features. As far as we are aware, our paper presents
the first approach to modeling the physics of acoustic propagation using deep
generative modeling. The VAE-SSL approach is compared with two signal
processing-based approaches, steered response power with phase transform
(SRP-PHAT) and MUltiple SIgnal Classification (MUSIC), as well as fully
supervised CNNs. We find that VAE-SSL can outperform the conventional
approaches and the CNN in label-limited scenarios. Further, the trained VAE-SSL
system can generate new RTF-phase samples, which shows the VAE-SSL approach
learns the physics of the acoustic environment. The generative modeling in
VAE-SSL thus provides a means of interpreting the learned representations.Comment: Revision, submitted to IEEE Acces
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