This thesis deals with processing signals from the microphone arrays for sound source localization. Compares different types of fields, such as cross-field and circular array. It is shown here how to implement Beamforming on FPGA and design of signal processing with a microphone array.

Sensor Fusion for 3D data is a popular topic. Multisensor data combination enhance the qualities of each other while single sensor lacks accuracy. In this thesis, an evaluation platform for Multimodal 3D data from Kinect v2 and Microphone Array is designed and implemented by using ReactJS. In automotive industry and computer vision area, 3D detection and localization are widely used. Solutions of 3D detection and localization using different measurement systems are discussed in a large number of papers. Data Fusion systems are normally using ultrasound based, radio waves based, Time-of-Flight, structured light, stereo cameras and sound based sensors. All of these measurement systems might provide different 3D data models. And each system works fine separately. However, in some cases, multiple measurement systems need to work together. Their 3D data sets are different and could not be compared and combined directly. In order to simplify the design process of multiple measurement systems, this web based evaluation platform is focused on comparison and combination of 3D data sets from different coordinate systems. It provides a quick and easy development method between multiple measurement systems. In this thesis, an evaluation platform which based on Kinect v2 body detection and microphone array sound detection systems will be discussed. First an introduction about project overview is given. The second section of this paper deals with several project related technologies. The third section provides the concept of this project. The forth section describes development and implement detail. The next section is about data visualization and statistical analysis. Further the final results, evaluation and discussion are given.

No description available.

<p dir="ltr">The design of hardware platform in acoustic sensing is critical. The number and the spatial arrangement of microphones play a huge role in sensing performance. All microphones should be properly processed for simultaneous recording. This work introduces an FPGA-based acoustic transceiver system supporting acoustic sensing with custom acoustic signals. The system contains 16 microphones and a speaker synchronized during sensing processes. The microphones were arranged into an ‘L’ shape with eight microphones on each line for a better resolution of angle estimation on two dimensions. The microphones were placed on a specifically designed PCB to achieve an optimal distance of the half-wavelength of acoustic signals for optimized sensing performance. A microphone interface was implemented on Ultra96-V2 FPGA for handling the simultaneous high-speed data streams. The system features an implementation of full-level data transmission up to the top-level Python program. To evaluate the sensing performance of the system, we conducted an experiment used Frequency Modulated Continuous Wave (FMCW) as the transmitted acoustic signal. The result of evaluation shown the accurate sensing of range, velocity and relative angle of a moving hand on the two dimensions corresponding to the microphone groups.</p>

In aeroacoustic experiments of aircraft models and/or components, arrays of microphones are utilized to spatially isolate distinct sources and mitigate interfering noise which contaminates single-microphone measurements. Array measurements are still biased by interfering noise which is coherent over the spatial array aperture. When interfering noise is accounted for, existing algorithms which aim to both spatially isolate distinct sources and determine their individual levels as measured by the array are complex and require assumptions about the nature of the sound field. This work develops a processing scheme which uses spatially-defined phase constraints to remove correlated, interfering noise at the single-channel level. This is achieved through a merger of Conditioned Spectral Analysis (CSA) and the Generalized Sidelobe Canceller (GSC). A cross-spectral, frequency-domain filter is created using the GSC methodology to edit the CSA formulation. The only constraint needed is the user-defined, relative phase difference between the channel being filtered and the reference channel used for filtering. This process, titled Constrained Spectral Conditioning (CSC), produces single-channel Fourier Transform estimates of signals which satisfy the user-defined phase differences. In a spatial sound field mapping context, CSC produces sub-datasets derived from the original which estimate the signal characteristics from distinct locations in space. Because single-channel Fourier Transforms are produced, CSC's outputs could theoretically be used as inputs to many existing algorithms. As an example, data-independent, frequency-domain beamforming (FDBF) using CSC's outputs is shown to exhibit finer spatial resolution and lower sidelobe levels than FDBF using the original, unmodified dataset. However, these improvements decrease with Signal-to-Noise Ratio (SNR), and CSC's quantitative accuracy is dependent upon accurate modeling of the sound propagation and inter-source coherence if multiple and/or distributed sources are measured. In order to demonstrate systematic spatial sound mapping using CSC, it is embedded into the CLEAN algorithm which is then titled CLEAN-CSC. Simulated data analysis indicates that CLEAN-CSC is biased towards the mapping and energy allocation of relatively stronger sources in the field, which limits its ability to identify and estimate the level of relatively weaker sources. It is also shown that CLEAN-CSC underestimates the true integrated levels of sources in the field and exhibits higher-than-true peak source levels, and these effects increase and decrease respectively with increasing frequency. Five independent scaling methods are proposed for correcting the CLEAN-CSC total integrated output levels, each with their own assumptions about the sound field being measured. As the entire output map is scaled, these do not account for relative source level errors that may exist. Results from two airfoil tests conducted in NASA Langley's Quiet Flow Facility show that CLEAN-CSC exhibits less map noise than CLEAN yet more segmented spatial sound distributions and lower integrated source levels. However, using the same source propagation model that CLEAN assumes, the scaled CLEAN-CSC integrated source levels are brought into closer agreement with those obtained with CLEAN. / Ph. D.

Vertical-axis wind turbines (VAWTs) have with time been outrivaled by the today more common and economically feasible horizontal-axis wind turbines (HAWTs). However, VAWTs have several advantages which still make them interesting, for example, the VAWTs can have the drive train at ground level and it has been argued that they have lower noise emission. Other proposed advantages are suitability for both up-scaling and floating offshore platforms. The work within this thesis is made in collaboration between Halmstad University and Uppsala University. A 200-kW semi-guy-wired VAWT H-rotor, owned by Uppsala University but situated in Falkenberg close to Halmstad, has been the main subject of the research although most results can be generalized to suit a typical H-rotor. This thesis has three main topics regarding VAWTs: (1) how the wind energy extraction is influenced by turbulence, (2) aerodynamical noise generation and (3) eigenfrequencies of the semi-guy-wired tower. The influence from turbulence on the wind energy extraction is studied by evaluating logged operational data and examining how the power curve and the tip-speed ratio for maximum Cp is impacted by turbulence. The work has showed that the T1-turbine has a good ability to extract wind energy at turbulent conditions, indicating an advantage in energy extraction at turbulent sites for VAWTs compared to HAWTs.The noise characteristics are studied experimentally, and models of the two most likely aerodynamic noise mechanisms are applied. Here, inflow-turbulence noise is deemed as the prevailing noise source rather than turbulent-boundary-layer trailing-edge noise (TBL-TE) which is the most important noise mechanism for HAWTs. The overall noise emission has also been measured and proven low compared to similar sized HAWTs. The eigenfrequencies of a semi-guy-wired tower are also studied. Analytical expressions describing the first-mode eigenfrequency of both tower and guy wire has been derived and verified by experiments and simulations.

The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

This project presents the description, design and the implementation of a 4-channel microphone array that is an adaptive sub-band generalized side lobe canceller (GSC) beam former uses for video conferencing, hands-free telephony etc, in a noisy environment for speech enhancement as well as noise suppression. The side lobe canceller evaluated with both Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) adaptation. A testing structure is presented; which involves a linear 4-microphone array connected to collect the data. Tests were done using one target signal source and one noise source. In each microphone’s, data were collected via fractional time delay filtering then it is divided into sub-bands and applied GSC to each of the subsequent sub-bands. The overall Signal to Noise Ratio (SNR) improvement is determined from the main signal and noise input and output powers, with signal-only and noise-only as the input to the GSC. The NLMS algorithm significantly improves the speech quality with noise suppression levels up to 13 dB while LMS algorithm is giving up to 10 dB. All of the processing for this thesis is implemented on a computer using MATLAB and validated by considering different SNR measure under various types of blocking matrix, different step sizes, different noise locations and variable SNR with noise. / Mamun Ahmed E-mail: mamuncse99cuet@yahoo.com

ABSTRACT The detection of an emergency signal can be estimated by the cancellation of surrounding noise and achieving the desired signal in order to alert the automobilist. The aim of the thesis is to detect the emergency signal arriving nearer to the automobilist carrying hearing aids. Recent studies show that this can be achieved by designing various kinds of fixed and adaptive beam formers. A beam former does spatial filtering in the sense that it separates two signals with overlapping frequency content originating from distinctive directions. In this contribution, robust beam former namely Wiener beam former is designed and analyzed collaboratively in a group under the consideration of hearing aid constraints such as the microphone distance. A fractionally delay (FD) are designed to get a maximally flat group delay. The studies had been carried out by comparing noise cancellation algorithms like LMS, NLMS, LLMS and RLS algorithms. By comparing Omni-directional and multi-directional microphones the SNR can be studied. In this thesis work, first proposing appropriate microphone array setup with improved beam forming techniques by using required adaptive algorithm (NLMS) in order to get better quality using the Microphone arrays. Microphone arrays have been widely used to improve the performance of speech recognition systems as well as to benefit for people who need hearing aids. With the help of microphone arrays, it can choose to focus on signals from a specific direction. To getting better signal quality in microphone array using adaptive algorithms, these are help in the noise suppression in accordance with the different beam forming techniques. The proposed system is implemented successfully and validated using MATLAB simulation tool. The emergency signal is different in different countries, so we identify any type of emergency signal by training through neural networks. / Vamshi Krishna Lakum: +46760190899

Acoustic Sound Source localization using signal processing is required in order to estimate the direction from where a particular acoustic source signal is coming and it is also important in order to find a soluation for hands free communication. Video conferencing, hand free communications are different applications requiring acoustic sound source localization. This applications need a robust algorithm which can reliably localize and position the acoustic sound sources. The Steered Response Power Phase Transform (SRP-PHAT) is an important and roubst algorithm to localilze acoustic sound sources. However, the algorithm has a high computational complexity thus making the algorithm unsuitable for real time applications. This thesis focuses on describe the implementation of the SRP-PHAT algorithm as a function of source type, reverberation levels and ambient noise. The main objective of this thesis is to present different approaches of the SRP-PHAT to verify the algorithm in terms of acoustic enviroment, microphone array configuration, acoustic source position and levels of reverberation and noise.