2,961 research outputs found
Broadband DOA estimation using Convolutional neural networks trained with noise signals
A convolution neural network (CNN) based classification method for broadband
DOA estimation is proposed, where the phase component of the short-time Fourier
transform coefficients of the received microphone signals are directly fed into
the CNN and the features required for DOA estimation are learnt during
training. Since only the phase component of the input is used, the CNN can be
trained with synthesized noise signals, thereby making the preparation of the
training data set easier compared to using speech signals. Through experimental
evaluation, the ability of the proposed noise trained CNN framework to
generalize to speech sources is demonstrated. In addition, the robustness of
the system to noise, small perturbations in microphone positions, as well as
its ability to adapt to different acoustic conditions is investigated using
experiments with simulated and real data.Comment: Published in Proceedings of IEEE Workshop on Applications of Signal
Processing to Audio and Acoustics (WASPAA) 201
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Direction of arrival (DoA) estimation is a fundamental task in array
processing. A popular family of DoA estimation algorithms are subspace methods,
which operate by dividing the measurements into distinct signal and noise
subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and
Root-MUSIC, rely on several restrictive assumptions, including narrowband
non-coherent sources and fully calibrated arrays, and their performance is
considerably degraded when these do not hold. In this work we propose
SubspaceNet; a data-driven DoA estimator which learns how to divide the
observations into distinguishable subspaces. This is achieved by utilizing a
dedicated deep neural network to learn the empirical autocorrelation of the
input, by training it as part of the Root-MUSIC method, leveraging the inherent
differentiability of this specific DoA estimator, while removing the need to
provide a ground-truth decomposable autocorrelation matrix. Once trained, the
resulting SubspaceNet serves as a universal surrogate covariance estimator that
can be applied in combination with any subspace-based DoA estimation method,
allowing its successful application in challenging setups. SubspaceNet is shown
to enable various DoA estimation algorithms to cope with coherent sources,
wideband signals, low SNR, array mismatches, and limited snapshots, while
preserving the interpretability and the suitability of classic subspace
methods.Comment: Under review for publication in the IEE
Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques
In this paper, we present a machine learning approach for estimating the
number of incident wavefronts in a direction of arrival scenario. In contrast
to previous works, a multilayer neural network with a cross-entropy objective
is trained. Furthermore, we investigate an online training procedure that
allows an adaption of the neural network to imperfections of an antenna array
without explicitly calibrating the array manifold. We show via simulations that
the proposed method outperforms classical model order selection schemes based
on information criteria in terms of accuracy, especially for a small number of
snapshots and at low signal-to-noise-ratios. Also, the online training
procedure enables the neural network to adapt with only a few online training
samples, if initialized by offline training on artificial data
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
We present a novel learning-based approach to estimate the
direction-of-arrival (DOA) of a sound source using a convolutional recurrent
neural network (CRNN) trained via regression on synthetic data and Cartesian
labels. We also describe an improved method to generate synthetic data to train
the neural network using state-of-the-art sound propagation algorithms that
model specular as well as diffuse reflections of sound. We compare our model
against three other CRNNs trained using different formulations of the same
problem: classification on categorical labels, and regression on spherical
coordinate labels. In practice, our model achieves up to 43% decrease in
angular error over prior methods. The use of diffuse reflection results in 34%
and 41% reduction in angular prediction errors on LOCATA and SOFA datasets,
respectively, over prior methods based on image-source methods. Our method
results in an additional 3% error reduction over prior schemes that use
classification based networks, and we use 36% fewer network parameters
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