Ines Hafizovic

Beamspace adaptive beamforming for ultrasound imaging

Applying the Capon adaptive beamformer in medical ultrasound imaging results in enhanced resolution by improving the interference-suppressing capabilities of the array. This improvement comes at the expense of an increased computational complexity. We have investigated the application of a beamspace adaptive beamformer for medical ultrasound imaging, which can be used to achieve reduced computational complexity with performance comparable to that of the Capon beamformer. The idea behind beamspace beamforming is that, instead of using the spatial statistics of the elements in the array to differentiate between signals and interference, we use the spatial statistics of a set of orthogonal beams, which are formed in different directions. This represents a shift from element space to beamspace. Because the majority of interference in medical ultrasound imaging is constrained to a limited spatial interval due to the focused transmit beam, this latter space can be reduced to a dimension that is lower than that of element space. We show, using simulations and experimental data, that this dimension can be selected as low as 3 while still achieving performance comparable to its element space counterpart.

Digital beamforming using a GPU

In this paper we investigate the use of GPUs as digital beamformers. We specify a parallel implementation of a beamformer in time and frequency domain and measure its performance. We also give examples of the processing limits of NVIDIA Geforce 8800 GPU with respect to application parameters: number of sensors, sampling frequency, bandwidth, and number of simultaneous beams. The results are compared to those of algorithms similarly implemented on a Intel Xeon CPU. We find that the GPU is able to process a larger amount of information than the CPU, and that it can be used as a digital beamformer for arrays with a large number of elements sampled at high rates. Exact results are given for the abovementioned application parameters.

Acoustic tracking of aircraft using a circular microphone array sensor

Runway Incursion Avoidance Systems (RIAS) are vital parts in the prevention of runway accidents at airports. Such systems depend on the accurate positioning of aircraft and ground vehicles. Today, RADAR is the predominant method, but a RADAR based system might be both impractical and too expensive for small airports. Additionally, the redundancy of several independent systems is sought after even at large airports. We investigate the application of a low-cost circular microphone array solution for acoustic detection and tracking of aircraft. The shape of the array yields 360° coverage with equal characteristics in all directions. An algorithm based on sweeps performed with the Capon adaptive beamformer followed by peak detection and thresholding is implemented for real time use. The performance of the system is tested at Evenes airport in Norway.

Multi-speaker voice activity detection using a camera-assisted microphone array

We present a method for voice activity detection of multiple concurrent speakers using a camera-assisted microphone array. The proposed method uses face detection to identify locations of potential speech sources, and uses this information in an adaptive beamforming procedure to form a spatially directed detection algorithm to identify voice activity for individual speakers. Voice activity is classified using support vector machines with mel-frequency cepstrum coefficients as features. To increase the spatial filtering ability of the array we use a combination of Dolph-Chebyshev weighting and null-steering. We have carried out two experiments to gauge the accuracy of the proposed method, and obtain a representative accuracy of around 95% for single speakers, with around 1% loss of accuracy with two simultaneous speakers.

Transformation Between Uniform Linear and Spherical Microphone Arrays With Symmetric Responses

Spherical microphone arrays are suited for phase mode processing, which complements spatio-temporal processing and often simplifies both the understanding and development of different beamforming techniques. Since a spherical geometry cannot benefit directly from methods specially developed for uniform linear arrays, the weight and algorithm design for spherical arrays is mostly optimized and found numerically. One of the exceptions is a recent study that develops the well-known Dolph-Chebyshev weights in the phase mode framework. We show that for the case of a symmetric response, the spherical and linear array geometries are one-to-one, related through a herein developed linear transformation. With this transformation, uniform linear array specific processing techniques become readily available in closed form for spherical array processing. Any uniform linear array weight design technique can be directly applied to spherical arrays. We also show how this transformation can be used to generate virtual uniform linear array data from spherical array data, enabling us to apply uniform linear array specific adaptive algorithms to spherical arrays.

Decorrelation for adaptive beamforming applied to arbitrarily sampled spherical microphone arrays

Correlated signals lead to signal cancellation in adaptive beamformers applied to microphone arrays. This is commonly counteracted by spatial smoothing. Unfortunately, spatial smoothing can only be used with array geometries consisting of identical, shifted subarrays, making it unsuitable for spherical arrays in general. We suggest a transformation that makes spatial smoothing applicable to any well-sampled spherical array, and we show results for the case of Minimum Variance Distortionless Response (MVDR) beamforming.

Acoustic imaging of sparse Sources with Orthogonal Matching Pursuit and clustering of basis vectors

We have devised a greedy method for finding solutions to the sparse Deconvolution Approach for the Mapping of Acoustic Sources inverse problem using a variant of Orthogonal Matching Pursuit. The algorithm has two stages, wherein the first stage consists of selecting a subset of the basis vectors iteratively via a regularized inverse of the point spread function, and the second stage consists of constructing point source solutions using this basis subset and its coefficients via hierarchical agglomerative clustering. We have evaluated the algorithm on both synthetic and real data, and show that the overall accuracy in terms of direction of arrival and reconstructed source power is better than four other state of the art methods.