Reverberation: models, estimation and application

The use of reverberation models is required in many applications such as acoustic measurements, speech dereverberation and robust automatic speech recognition. The aim of this thesis is to investigate different models and propose a perceptually-relevant reverberation model with suitable parameter estimation techniques for different applications. Reverberation can be modelled in both the time and frequency domain. The model parameters give direct information of both physical and perceptual characteristics. These characteristics create a multidimensional parameter space of reverberation, which can be to a large extent captured by a time-frequency domain model. In this thesis, the relationship between physical and perceptual model parameters will be discussed. In the first application, an intrusive technique is proposed to measure the reverberation or reverberance, perception of reverberation and the colouration. The room decay rate parameter is of particular interest. In practical applications, a blind estimate of the decay rate of acoustic energy in a room is required. A statistical model for the distribution of the decay rate of the reverberant signal named the eagleMax distribution is proposed. The eagleMax distribution describes the reverberant speech decay rates as a random variable that is the maximum of the room decay rates and anechoic speech decay rates. Three methods were developed to estimate the mean room decay rate from the eagleMax distributions alone. The estimated room decay rates form a reverberation model that will be discussed in the context of room acoustic measurements, speech dereverberation and robust automatic speech recognition individually

Blind identification of acoustic systems and enhancement of reverberant speech

Imperial Users onl

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments

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

Beiträge zu breitbandigen Freisprechsystemen und ihrer Evaluation

This work deals with the advancement of wideband hands-free systems (HFS’s) for mono- and stereophonic cases of application. Furthermore, innovative contributions to the corr. field of quality evaluation are made. The proposed HFS approaches are based on frequency-domain adaptive filtering for system identification, making use of Kalman theory and state-space modeling. Functional enhancement modules are developed in this work, which improve one or more of key quality aspects, aiming at not to harm others. In so doing, these modules can be combined in a flexible way, dependent on the needs at hand. The enhanced monophonic HFS is evaluated according to automotive ITU-T recommendations, to prove its customized efficacy. Furthermore, a novel methodology and techn. framework are introduced in this work to improve the prototyping and evaluation process of automotive HF and in-car-communication (ICC) systems. The monophonic HFS in several configurations hereby acts as device under test (DUT) and is thoroughly investigated, which will show the DUT’s satisfying performance, as well as the advantages of the proposed development process. As current methods for the evaluation of HFS’s in dynamic conditions oftentimes still lack flexibility, reproducibility, and accuracy, this work introduces “Car in a Box” (CiaB) as a novel, improved system for this demanding task. It is able to enhance the development process by performing high-resolution system identification of dynamic electro-acoustical systems. The extracted dyn. impulse response trajectories are then applicable to arbitrary input signals in a synthesis operation. A realistic dynamic automotive auralization of a car cabin interior is available for HFS evaluation. It is shown that this system improves evaluation flexibility at guaranteed reproducibility. In addition, the accuracy of evaluation methods can be increased by having access to exact, realistic imp. resp. trajectories acting as a so-called “ground truth” reference. If CiaB is included into an automotive evaluation setup, there is no need for an acoustical car interior prototype to be present at this stage of development. Hency, CiaB may ease the HFS development process. Dynamic acoustic replicas may be provided including an arbitrary number of acoustic car cabin interiors for multiple developers simultaneously. With CiaB, speech enh. system developers therefore have an evaluation environment at hand, which can adequately replace the real environment.Diese Arbeit beschäftigt sich mit der Weiterentwicklung breitbandiger Freisprechsysteme für mono-/stereophone Anwendungsfälle und liefert innovative Beiträge zu deren Qualitätsmessung. Die vorgestellten Verfahren basieren auf im Frequenzbereich adaptierenden Algorithmen zur Systemidentifikation gemäß Kalman-Theorie in einer Zustandsraumdarstellung. Es werden funktionale Erweiterungsmodule dahingehend entwickelt, dass mindestens eine Qualitätsanforderung verbessert wird, ohne andere eklatant zu verletzen. Diese nach Anforderung flexibel kombinierbaren algorithmischen Erweiterungen werden gemäß Empfehlungen der ITU-T (Rec. P.1110/P.1130) in vorwiegend automotiven Testszenarien getestet und somit deren zielgerichtete Wirksamkeit bestätigt. Es wird eine Methodensammlung und ein technisches System zur verbesserten Prototypentwicklung/Evaluation von automotiven Freisprech- und Innenraumkommunikationssystemen vorgestellt und beispielhaft mit dem monophonen Freisprechsystem in diversen Ausbaustufen zur Anwendung gebracht. Daraus entstehende Vorteile im Entwicklungs- und Testprozess von Sprachverbesserungssystem werden dargelegt und messtechnisch verifiziert. Bestehende Messverfahren zum Verhalten von Freisprechsystemen in zeitvarianten Umgebungen zeigten bisher oft nur ein unzureichendes Maß an Flexibilität, Reproduzierbarkeit und Genauigkeit. Daher wird hier das „Car in a Box“-Verfahren (CiaB) entwickelt und vorgestellt, mit dem zeitvariante elektro-akustische Systeme technisch identifiziert werden können. So gewonnene dynamische Impulsantworten können im Labor in einer Syntheseoperation auf beliebige Eingangsignale angewandt werden, um realistische Testsignale unter dyn. Bedingungen zu erzeugen. Bei diesem Vorgehen wird ein hohes Maß an Flexibilität bei garantierter Reproduzierbarkeit erlangt. Es wird gezeigt, dass die Genauigkeit von darauf basierenden Evaluationsverfahren zudem gesteigert werden kann, da mit dem Vorliegen von exakten, realen Impulsantworten zu jedem Zeitpunkt der Messung eine sogenannte „ground truth“ als Referenz zur Verfügung steht. Bei der Einbindung von CiaB in einen Messaufbau für automotive Freisprechsysteme ist es bedeutsam, dass zu diesem Zeitpunkt das eigentliche Fahrzeug nicht mehr benötigt wird. Es wird gezeigt, dass eine dyn. Fahrzeugakustikumgebung, wie sie im Entwicklungsprozess von automotiven Sprachverbesserungsalgorithmen benötigt wird, in beliebiger Anzahl vollständig und mind. gleichwertig durch CiaB ersetzt werden kann

Sensor array signal processing : two decades later

Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg

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