A Methodology for Acoustic Measurement and Separation of Background Noise

An attempt is made at developing experimental methods for the acoustic measurement and separation of background noise in a wind tunnel. To this end, an array beamforming technique known as delay-and- sum beamforming is identified and tested. The theory underlying delay-and-sum beamforming is discussed. Two linear arrays, the seven microphone linear array and the four microphone minimum redundancy array, are designed. A driver is designed based on Helmholtz resonator theory to provide a source of monochromatic sound. Also, the concept of partial coherence as applicable to the separation of background noise from signal noise is investigated. Array beamforming results show that tests conducted with the two linear arrays in the open field provide good resolution of the sound source Direction Of Arrival (DOA) peaks from the background noise, and provide a semianechoic reference with which to compare wind tunnel results. Beamforming results obtained for the driver placed inside the wind tunnel with the tunnel running at 0, 45, and 81 ft/sec successfully resolved the DOA peaks of the driver from the background noise of the tunnel. At a tunnel velocity of 151 ft/sec, the driver signal is completely buried in the background noise of the tunnel, and beamforming was not successful in resolving the peak corresponding to the driver signal

Adaptive array processing for multiple microphone hearing aids

Also issued as Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Includes bibliographical references.Supported in part by the National Institutes of Neurological and Communicative Disorders and Stroke of the National Institutes of Health. RO1-NS21322Patrick M. Peterson

PSD Estimation and Source Separation in a Noisy Reverberant Environment using a Spherical Microphone Array

In this paper, we propose an efficient technique for estimating individual power spectral density (PSD) components, i.e., PSD of each desired sound source as well as of noise and reverberation, in a multi-source reverberant sound scene with coherent background noise. We formulate the problem in the spherical harmonics domain to take the advantage of the inherent orthogonality of the spherical harmonics basis functions and extract the PSD components from the cross-correlation between the different sound field modes. We also investigate an implementation issue that occurs at the nulls of the Bessel functions and offer an engineering solution. The performance evaluation takes place in a practical environment with a commercial microphone array in order to measure the robustness of the proposed algorithm against all the deviations incurred in practice. We also exhibit an application of the proposed PSD estimator through a source septation algorithm and compare the performance with a contemporary method in terms of different objective measures

Implementation and evaluation of a low complexity microphone array for speaker recognition

Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field

Broadband adaptive beamforming with low complexity and frequency invariant response

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation.A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save based GSC beamforming structures have been explored. This system address the minimisation of the time domain MMSE, with a significant reduction in computational complexity when compared to time-domain implementations, and show a better convergence behaviour than the IFB beamformer. By studying the effects that the blocking matrix has on the adaptive process for the overlap-save beamformer, several modifications are carried out to enhance both the simplicity of the algorithm as well as its convergence speed. These modifications result in the GSC beamformer utilising a significantly lower computational complexity compare to the time domain approach while offering similar convergence characteristics.In certain applications, especially in the areas of acoustics, there is a need to maintain constant resolution across a wide operating spectrum that may extend across several octaves. To attain constant beamwidth is difficult, particularly if uniformly spaced linear sensor array are employed for beamforming, since spatial resolution is reciprocally proportional to both the array aperture and the frequency. A scaled aperture arrangement is introduced for the subband based GSC beamformer to achieve near uniform resolution across a wide spectrum, whereby an octave-invariant design is achieved. This structure can also be operated in conjunction with adaptive beamforming algorithms. Frequency dependent tapering of the sensor signals is proposed in combination with the overlap-save GSC structure in order to achieve an overall frequency-invariant characteristic. An adaptive version is proposed for frequency-invariant overlap-save GSC beamformer. Broadband adaptive beamforming algorithms based on the family of least mean squares (LMS) algorithms are known to exhibit slow convergence if the input signal is correlated. To improve the convergence of the GSC when based on LMS-type algorithms, we propose the use of a broadband eigenvalue decomposition (BEVD) to decorrelate the input of the adaptive algorithm in the spatial dimension, for which an increase in convergence speed can be demonstrated over other decorrelating measures, such as the Karhunen-Loeve transform. In order to address the remaining temporal correlation after BEVD processing, this approach is combined with subband decomposition through the use of oversampled filter banks. The resulting spatially and temporally decorrelated GSC beamformer provides further enhanced convergence speed over spatial or temporal decorrelation methods on their own

Spatial dissection of a soundfield using spherical harmonic decomposition

A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches. To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield. We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset

Engine Validation of Noise and Emission Reduction Technology Phase I

This final report has been prepared by Honeywell Aerospace, Phoenix, Arizona, a unit of Honeywell International, Inc., documenting work performed during the period December 2004 through August 2007 for the NASA Glenn Research Center, Cleveland, Ohio, under the Revolutionary Aero-Space Engine Research (RASER) Program, Contract No. NAS3-01136, Task Order 8, Engine Validation of Noise and Emission Reduction Technology Phase I. The NASA Task Manager was Dr. Joe Grady of the NASA Glenn Research Center. The NASA Contract Officer was Mr. Albert Spence of the NASA Glenn Research Center. This report is for a test program in which NASA funded engine validations of integrated technologies that reduce aircraft engine noise. These technologies address the reduction of engine fan and jet noise, and noise associated with propulsion/airframe integration. The results of these tests will be used by NASA to identify the engineering tradeoffs associated with the technologies that are needed to enable advanced engine systems to meet stringent goals for the reduction of noise. The objectives of this program are to (1) conduct system engineering and integration efforts to define the engine test-bed configuration; (2) develop selected noise reduction technologies to a technical maturity sufficient to enable engine testing and validation of those technologies in the FY06-07 time frame; (3) conduct engine tests designed to gain insight into the sources, mechanisms and characteristics of noise in the engines; and (4) establish baseline engine noise measurements for subsequent use in the evaluation of noise reduction