485 research outputs found
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
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments
We propose a spatial diffuseness feature for deep neural network (DNN)-based
automatic speech recognition to improve recognition accuracy in reverberant and
noisy environments. The feature is computed in real-time from multiple
microphone signals without requiring knowledge or estimation of the direction
of arrival, and represents the relative amount of diffuse noise in each time
and frequency bin. It is shown that using the diffuseness feature as an
additional input to a DNN-based acoustic model leads to a reduced word error
rate for the REVERB challenge corpus, both compared to logmelspec features
extracted from noisy signals, and features enhanced by spectral subtraction.Comment: accepted for ICASSP201
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
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
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