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

A Nested Sensor Array Focusing on Near Field Targets

A nested virtual array subband beamforming system is proposed for applications where broadband signal targets are located within the near field of the array. Subband multirate processing and near field beamforming techniques are used jointly for the nested array to improve the performances and reduce the computational complexity. A new noise model, namely the broadband near field spherically isotropic noise model, is also proposed for the optimization design of near field beamformers. It is shown that near field beamforming is essential for better distance discrimination of near field targets, reduced beampattern variations for broadband signals, and stronger reverberation suppression

Paraunitary oversampled filter bank design for channel coding

Oversampled filter banks (OSFBs) have been considered for channel coding, since their redundancy can be utilised to permit the detection and correction of channel errors. In this paper, we propose an OSFB-based channel coder for a correlated additive Gaussian noise channel, of which the noise covariance matrix is assumed to be known. Based on a suitable factorisation of this matrix, we develop a design for the decoder's synthesis filter bank in order to minimise the noise power in the decoded signal, subject to admitting perfect reconstruction through paraunitarity of the filter bank. We demonstrate that this approach can lead to a significant reduction of the noise interference by exploiting both the correlation of the channel and the redundancy of the filter banks. Simulation results providing some insight into these mechanisms are provided

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

Array signal processing algorithms for localization and equalization in complex acoustic channels

The reproduction of realistic soundscapes in consumer electronic applications has been a driving force behind the development of spatial audio signal processing techniques. In order to accurately reproduce or decompose a particular spatial sound field, being able to exploit or estimate the effects of the acoustic environment becomes essential. This requires both an understanding of the source of the complexity in the acoustic channel (the acoustic path between a source and a receiver) and the ability to characterize its spatial attributes. In this thesis, we explore how to exploit or overcome the effects of the acoustic channel for sound source localization and sound field reproduction. The behaviour of a typical acoustic channel can be visualized as a transformation of its free field behaviour, due to scattering and reflections off the measurement apparatus and the surfaces in a room. These spatial effects can be modelled using the solutions to the acoustic wave equation, yet the physical nature of these scatterers typically results in complex behaviour with frequency. The first half of this thesis explores how to exploit this diversity in the frequency-domain for sound source localization, a concept that has not been considered previously. We first extract down-converted subband signals from the broadband audio signal, and collate these signals, such that the spatial diversity is retained. A signal model is then developed to exploit the channel's spatial information using a signal subspace approach. We show that this concept can be applied to multi-sensor arrays on complex-shaped rigid bodies as well as the special case of binaural localization. In both c! ases, an improvement in the closely spaced source resolution is demonstrated over traditional techniques, through simulations and experiments using a KEMAR manikin. The binaural analysis further indicates that the human localization performance in certain spatial regions is limited by the lack of spatial diversity, as suggested in perceptual experiments in the literature. Finally, the possibility of exploiting known inter-subband correlated sources (e.g., speech) for localization in under-determined systems is demonstrated. The second half of this thesis considers reverberation control, where reverberation is modelled as a superposition of sound fields created by a number of spatially distributed sources. We consider the mode/wave-domain description of the sound field, and propose modelling the reverberant modes as linear transformations of the desired sound field modes. This is a novel concept, as we consider each mode transformation to be independent of other modes. This model is then extended to sound field control, and used to derive the compensation signals required at the loudspeakers to equalize the reverberation. We show that estimating the reverberant channel and controlling the sound field now becomes a single adaptive filtering problem in the mode-domain, where the modes can be adapted independently. The performance of the proposed method is compared with existing adaptive and non-adaptive sound field control techniques through simulations. Finally, it is shown that an order of magnitude reduction in the computational complexity can be achieved, while maintaining comparable performance to existing adaptive control techniques

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