A Microphone Array System for Multimedia Applications with Near-Field Signal Targets

A microphone array beamforming system is proposed for multimedia communication applications using four sets of small planar arrays mounted on a computer monitor. A new virtual array approach is employed such that the original signals received by the array elements are weighted and delayed to synthesize a large, nonuniformly spaced, harmonically nested virtual array covering the frequency band [50, 7000] Hz of the wideband telephony. Subband multirate processing and near-field beamforming techniques are then used jointly by the nested virtual array to improve the performances in reverberant environments. A new beamforming algorithm is also proposed using a broadband near-field spherically isotropic noise model for array optimization. The near-field noise model assumes a large number of broadband random noises uniformly distributed over a sphere with a finite radius in contrast to the conventional far-field isotropic noise model which has an infinite radius. The radius of the noise model, thus, adds a design parameter in addition to its power for tradeoffs between performance and robustness. It is shown that the near-field beamformers designed by the new algorithm can achieve more than 8-dB reverberation suppression while maintaining sufficient robustness against background noises and signal location errors. Computer simulations and real room experiments also show that the proposed array beamforming system reduces beampattern variations for broadband signals, obtains strong noise and reverberation suppression, and improves the sound quality for near-field targets

Robust Near-Field Adaptive Beamforming with Distance Discrimination

This paper proposes a robust near-field adaptive beamformer for microphone array applications in small rooms. Robustness against location errors is crucial for near-field adaptive beamforming due to the difficulty in estimating near-field signal locations especially the radial distances. A near-field regionally constrained adaptive beamformer is proposed to design a set of linear constraints by filtering on a low rank subspace of the near-field signal over a spatial region and frequency band such that the beamformer response over the designed spatial-temporal region can be accurately controlled by a small number of linear constraint vectors. The proposed constraint design method is a systematic approach which guarantees real arithmetic implementation and direct time domain algorithms for broadband beamforming. It improves the robustness against large errors in distance and directions of arrival, and achieves good distance discrimination simultaneously. We show with a nine-element uniform linear array that the proposed near-field adaptive beamformer is robust against distance errors as large as ±32% of the presumed radial distance and angle errors up to ±20⁰. It can suppress a far field interfering signal with the same angle of incidence as a near-field target by more than 20 dB with no loss of the array gain at the near-field target. The significant distance discrimination of the proposed near-field beamformer also helps to improve the dereverberation gain and reduce the desired signal cancellation in reverberant environments

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

Acoustic Speaker Localization with Strong Reverberation and Adaptive Feature Filtering with a Bayes RFS Framework

The thesis investigates the challenges of speaker localization in presence of strong reverberation, multi-speaker tracking, and multi-feature multi-speaker state filtering, using sound recordings from microphones. Novel reverberation-robust speaker localization algorithms are derived from the signal and room acoustics models. A multi-speaker tracking filter and a multi-feature multi-speaker state filter are developed based upon the generalized labeled multi-Bernoulli random finite set framework. Experiments and comparative studies have verified and demonstrated the benefits of the proposed methods

Acoustic event detection and localization using distributed microphone arrays

Automatic acoustic scene analysis is a complex task that involves several functionalities: detection (time), localization (space), separation, recognition, etc. This thesis focuses on both acoustic event detection (AED) and acoustic source localization (ASL), when several sources may be simultaneously present in a room. In particular, the experimentation work is carried out with a meeting-room scenario. Unlike previous works that either employed models of all possible sound combinations or additionally used video signals, in this thesis, the time overlapping sound problem is tackled by exploiting the signal diversity that results from the usage of multiple microphone array beamformers. The core of this thesis work is a rather computationally efficient approach that consists of three processing stages. In the first, a set of (null) steering beamformers is used to carry out diverse partial signal separations, by using multiple arbitrarily located linear microphone arrays, each of them composed of a small number of microphones. In the second stage, each of the beamformer output goes through a classification step, which uses models for all the targeted sound classes (HMM-GMM, in the experiments). Then, in a third stage, the classifier scores, either being intra- or inter-array, are combined using a probabilistic criterion (like MAP) or a machine learning fusion technique (fuzzy integral (FI), in the experiments). The above-mentioned processing scheme is applied in this thesis to a set of complexity-increasing problems, which are defined by the assumptions made regarding identities (plus time endpoints) and/or positions of sounds. In fact, the thesis report starts with the problem of unambiguously mapping the identities to the positions, continues with AED (positions assumed) and ASL (identities assumed), and ends with the integration of AED and ASL in a single system, which does not need any assumption about identities or positions. The evaluation experiments are carried out in a meeting-room scenario, where two sources are temporally overlapped; one of them is always speech and the other is an acoustic event from a pre-defined set. Two different databases are used, one that is produced by merging signals actually recorded in the UPC¿s department smart-room, and the other consists of overlapping sound signals directly recorded in the same room and in a rather spontaneous way. From the experimental results with a single array, it can be observed that the proposed detection system performs better than either the model based system or a blind source separation based system. Moreover, the product rule based combination and the FI based fusion of the scores resulting from the multiple arrays improve the accuracies further. On the other hand, the posterior position assignment is performed with a very small error rate. Regarding ASL and assuming an accurate AED system output, the 1-source localization performance of the proposed system is slightly better than that of the widely-used SRP-PHAT system, working in an event-based mode, and it even performs significantly better than the latter one in the more complex 2-source scenario. Finally, though the joint system suffers from a slight degradation in terms of classification accuracy with respect to the case where the source positions are known, it shows the advantage of carrying out the two tasks, recognition and localization, with a single system, and it allows the inclusion of information about the prior probabilities of the source positions. It is worth noticing also that, although the acoustic scenario used for experimentation is rather limited, the approach and its formalism were developed for a general case, where the number and identities of sources are not constrained

A study into the design of steerable microphones arrays

Beamforming, being a multi-channel signal processing technique, can offer both spatial and temporal selective filtering. It has much more potential than single channel signal processing in various commercial applications. This thesis presents a study on steerable robust broadband beamformers together with a number of their design formulations. The design formulations allow a simple steering mechanism and yet maintain a frequency invariant property as well as achieve robustness against practical imperfectio

Robust Multichannel Microphone Beamforming

In this thesis, a method for the design and implementation of a spatially robust multichannel microphone beamforming system is presented. A set of spatial correlation functions are derived for 2D and 3D far-field/near-field scenarios based on von Mises(-Fisher), Gaussian, and uniform source location distributions. These correlation functions are used to design spatially robust beamformers and blocking beamformers (nullformers) designed to enhance or suppress a known source, where the target source location is not perfectly known due to either an incorrect location estimate or movement of the target while the beamformers are active. The spatially robust beam/null-formers form signal and interferer plus noise references which can be further processed via a blind source separation algorithm to remove mutual components - removing the interference and sensor noise from the signal path and vice versa. The noise reduction performance of the combined beamforming and blind source separation system approaches that of a perfect information MVDR beamformer under reverberant conditions. It is demonstrated that the proposed algorithm can be implemented on low-power hardware with good performance on hardware similar to current mobile platforms using a four-element microphone array

The measurement of underwater acoustic noise radiated by a vessel using the vessel's own towed array

The work described in this thesis tested the feasibility of using a towed array of hydrophones to: 1. localise sources of underwater acoustic noise radiated by the towvessel, 2. determine the absolute amplitudes of these sources, and 3. determine the resulting far-field acoustic signature of the tow-vessel. The concept was for the towvessel to carry out a U-turn manoeuvre so as to bring the acoustic section of the array into a location suitable for beamforming along the length of the tow-vessel. All three of the above were shown to be feasible using both simulated and field data, although no independent field measurements were available to fully evaluate the accuracy of the far-field acoustic signature determinations. A computer program was written to simulate the acoustic signals received by moving hydrophones. This program had the ability to model a variety of acoustic sources and to deal with realistic acoustic propagation conditions, including shallow water propagation with significant bottom interactions. The latter was accomplished using both ray and wave methods and it was found that, for simple fluid half-space seabeds, a modified ray method gave results that were virtually identical to those obtained with a full wave method, even at very low frequencies, and with a substantial saving in execution time. A field experiment was carried out during which a tug towing a 60-hydrophone array carried out a series of U-turn manoeuvres. The signals received by the array included noise radiated by the tow-vessel, signals from acoustic tracking beacons mounted on the tow-vessel, and transient signals generated by imploding sources deployed from a second vessel.Algorithms were developed to obtain snapshots of the vertical plane and horizontal plane shapes of the array from the transient data and to use range data derived from the tracking beacon signals to track the hydrophones in the horizontal plane. The latter was complicated by a high proportion of dropouts and outliers in the range data caused by the directionality of the hydrophones at the high frequencies emitted by the beacons. Despite this, excellent tracking performance was obtained. Matched field inversion was used to determine the vertical plane array shapes at times when no transient signals were available, and to provide information about the geoacoustic properties of the seabed. There was very good agreement between the inversion results and array shapes determined using transient signals. During trial manoeuvres the array was moving rapidly relative to the vessel and changing shape. A number of different array-processing algorithms were developed to provide source localisation and amplitude estimates in this situation: a timedomain beamformer; two frequency-domain, data independent beamformers; an adaptive frequency-domain beamformer; and an array processor based on a regularised least-squares inversion. The relative performance of each of these algorithms was assessed using simulated and field data. Data from three different manoeuvres were processed and in each case a calibrated source was localised to within 1 m of its known position at the source's fundamental frequency of 112 Hz.Localisation was also successful in most instances at 336 Hz, 560 Hz and 784 Hz, although with somewhat reduced accuracy due to lower signal to noise ratios. Localisation results for vessel noise sources were also consistent with the positions of the corresponding items of machinery. The estimated levels of the calibrated source obtained during the three manoeuvres were all within 4.1 dB of the calibrated value, and varied by only 1.3 dB between manoeuvres. Results at the higher frequencies had larger errors, with a maximum variation of 3.8 dB between serials, and a maximum deviation from the calibrated value of 6.8 dB. An algorithm was also developed to predict the far-field signature of the tow-vessel from the measured data and results were produced. This algorithm performed well with simulated data but no independent measurements were available to compare with the field results