2 research outputs found
Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Networks
We present an approach to deep neural network based (DNN-based) distance
estimation in reverberant rooms for supporting geometry calibration tasks in
wireless acoustic sensor networks. Signal diffuseness information from acoustic
signals is aggregated via the coherent-to-diffuse power ratio to obtain a
distance-related feature, which is mapped to a source-to-microphone distance
estimate by means of a DNN. This information is then combined with
direction-of-arrival estimates from compact microphone arrays to infer the
geometry of the sensor network. Unlike many other approaches to geometry
calibration, the proposed scheme does only require that the sampling clocks of
the sensor nodes are roughly synchronized. In simulations we show that the
proposed DNN-based distance estimator generalizes to unseen acoustic
environments and that precise estimates of the sensor node positions are
obtained.Comment: Accepted for EUSIPCO 202
Lightweight and Optimized Sound Source Localization and Tracking Methods for Open and Closed Microphone Array Configurations
Human-robot interaction in natural settings requires filtering out the
different sources of sounds from the environment. Such ability usually involves
the use of microphone arrays to localize, track and separate sound sources
online. Multi-microphone signal processing techniques can improve robustness to
noise but the processing cost increases with the number of microphones used,
limiting response time and widespread use on different types of mobile robots.
Since sound source localization methods are the most expensive in terms of
computing resources as they involve scanning a large 3D space, minimizing the
amount of computations required would facilitate their implementation and use
on robots. The robot's shape also brings constraints on the microphone array
geometry and configurations. In addition, sound source localization methods
usually return noisy features that need to be smoothed and filtered by tracking
the sound sources. This paper presents a novel sound source localization
method, called SRP-PHAT-HSDA, that scans space with coarse and fine resolution
grids to reduce the number of memory lookups. A microphone directivity model is
used to reduce the number of directions to scan and ignore non significant
pairs of microphones. A configuration method is also introduced to
automatically set parameters that are normally empirically tuned according to
the shape of the microphone array. For sound source tracking, this paper
presents a modified 3D Kalman (M3K) method capable of simultaneously tracking
in 3D the directions of sound sources. Using a 16-microphone array and low cost
hardware, results show that SRP-PHAT-HSDA and M3K perform at least as well as
other sound source localization and tracking methods while using up to 4 and 30
times less computing resources respectively