608 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
PSD Estimation of Multiple Sound Sources in a Reverberant Room Using a Spherical Microphone Array
We propose an efficient method to estimate source power spectral densities
(PSDs) in a multi-source reverberant environment using a spherical microphone
array. The proposed method utilizes the spatial correlation between the
spherical harmonics (SH) coefficients of a sound field to estimate source PSDs.
The use of the spatial cross-correlation of the SH coefficients allows us to
employ the method in an environment with a higher number of sources compared to
conventional methods. Furthermore, the orthogonality property of the SH basis
functions saves the effort of designing specific beampatterns of a conventional
beamformer-based method. We evaluate the performance of the algorithm with
different number of sources in practical reverberant and non-reverberant rooms.
We also demonstrate an application of the method by separating source signals
using a conventional beamformer and a Wiener post-filter designed from the
estimated PSDs.Comment: Accepted for WASPAA 201
- …