CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Towards low power radio localisation

This work investigates the use of super-resolution algorithms for precision localisation and long-term tracking of small subjects, like rodents. An overview is given of a variety of techniques for positioning in use today, namely received signal strength, time of arrival, time difference of arrival and direction of arrival (DoA). Based on the analysis, it is concluded that the direction finding signal subspace based techniques are most appropriate for the purposes of our system. The details of the software defined radio (SDR) antenna array testbed development, build, characterisation and performance evaluation are presented. The results of direction finding experiments in the screened anechoic chamber emulating open-space propagation are discussed. It is shown that such testbed is capable of locating sources in the vicinity of the array with high precision. It can estimate the DoAs of more simultaneously working transmitters than antennas in the array, by employing spread spectrum techniques, and readily accommodates very low power sources. Overall constraints on the system are such that the operational range must be around 50 – 100 m. The transmitter must be small both volumetrically and in terms of weight. It also has to be operational over an extended period of around 1 year. The implications of these are that very small antennas and batteries must be used, which are usually accompanied by very low transmission efficiencies and tiny capacities, respectively. Based on the above, the use of ultra-low power oscillator transmitters, as first cut prototypes of the tag, is proposed. It is shown that the Clapp, Colpitts, Pierce and Cross-coupled architectures are adequate. A thorough analysis of these topologies is provided with full details of tag and antenna co-design. Finally the performance of these architectures is evaluated through simulations with respect to power output, overall efficiency and phase noise.Open Acces

Performance Comparison Between Music And Esprit Algorithms For Direction Estimation Of Arrival Signals

This thesis examines and compares the performance of Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) for the estimation of Direction of Arrival (DOA) of incoming signals to the smart antenna. The comparison of these two algorithms was done on the basis of parameters like number of array elements, number of incoming signals, angle difference between the incoming signals, number of the samples taken of signal, processing time and SNR ratio. These two algorithms were implemented with MATLAB and SIMULINK for the experimental purpose. After all the experiments performed, it was analyzed that results obtained from both of the software were almost same. Comparing MUSIC\u27s results with ESPRIT, it was found that MUSIC is less prone to error than ESPRIT for almost all parametric tests. This superiority of MUSIC made it desirable to recommend it for DOA estimation in smart antenna system

Symphony: Localizing Multiple Acoustic Sources with a Single Microphone Array

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach