Äänikentän tila-analyysi parametrista tilaäänentoistoa varten käyttäen harvoja mikrofoniasetelmia

In spatial audio capturing the aim is to store information about the sound field so that the sound field can be reproduced without a perceptual difference to the original. The need for this is in applications like virtual reality and teleconferencing. Traditionally the sound field has been captured with a B-format microphone, but it is not always a feasible solution due to size and cost constraints. Alternatively, also arrays of omnidirectional microphones can be utilized and they are often used in devices like mobile phones. If the microphone array is sparse, i.e., the microphone spacings are relatively large, the analysis of the sound Direction of Arrival (DoA) becomes ambiguous in higher frequencies. This is due to spatial aliasing, which is a common problem in narrowband DoA estimation. In this thesis the spatial aliasing problem was examined and its effect on DoA estimation and spatial sound synthesis with Directional Audio Coding (DirAC) was studied. The aim was to find methods for unambiguous narrowband DoA estimation. The current State of the Art methods can remove aliased estimates but are not capable of estimating the DoA with the optimal Time-Frequency resolution. In this thesis similar results were obtained with parameter extrapolation when only a single broadband source exists. The main contribution of this thesis was the development of a correlation-based method. The developed method utilizes pre-known, array-specific information on aliasing in each DoA and frequency. The correlation-based method was tested and found to be the best option to overcome the problem of spatial aliasing. This method was able to resolve spatial aliasing even with multiple sources or when the source’s frequency content is completely above the spatial aliasing frequency. In a listening test it was found that the correlation-based method could provide a major improvement to the DirAC synthesized spatial image quality when compared to an aliased estimator.Tilaäänen tallentamisessa tavoitteena on tallentaa äänikentän ominaisuudet siten, että äänikenttä pystytään jälkikäteen syntetisoimaan ilman kuuloaistilla havaittavaa eroa alkuperäiseen. Tarve tälle löytyy erilaisista sovelluksista, kuten virtuaalitodellisuudesta ja telekonferensseista. Perinteisesti äänikentän ominaisuuksia on tallennettu B-formaatti mikrofonilla, jonka käyttö ei kuitenkaan aina ole koko- ja kustannussyistä mahdollista. Vaihtoehtoisesti voidaan käyttää myös pallokuvioisista mikrofoneista koostuvia mikrofoniasetelmia. Mikäli mikrofonien väliset etäisyydet ovat liian suuria, eli asetelma on harva, tulee äänen saapumissuunnan selvittämisestä epäselvää korkeammilla taajuuksilla. Tämä johtuu ilmiöstä nimeltä tilallinen laskostuminen. Tämän diplomityön tarkoituksena oli tutkia tilallisen laskostumisen ilmiötä, sen vaikutusta saapumissuunnan arviointiin sekä tilaäänisynteesiin Directional Audio Coding (DirAC) -menetelmällä. Lisäksi tutkittiin menetelmiä, joiden avulla äänen saapumissuunta voitaisiin selvittää oikein myös tilallisen laskostumisen läsnä ollessa. Työssä havaittiin, että nykyiset ratkaisut laskostumisongelmaan eivät kykene tuottamaan oikeita suunta-arvioita optimaalisella aikataajuusresoluutiolla. Tässä työssä samantapaisia tuloksia saatiin laajakaistaisen äänilähteen tapauksessa ekstrapoloimalla suunta-arvioita laskostumisen rajataajuuden alapuolelta. Työn pääosuus oli kehittää korrelaatioon perustuva saapumissuunnan arviointimenetelmä, joka kykenee tuottamaan luotettavia arvioita rajataajuuden yläpuolella ja useamman äänilähteen ympäristöissä. Kyseinen menetelmä hyödyntää mikrofoniasetelmalle ominaista, saapumissuunnasta ja taajuudesta riippuvaista laskostumiskuviota. Kuuntelukokeessa havaittiin, että korrelaatioon perustuva menetelmä voi tuoda huomattavan parannuksen syntetisoidun tilaäänikuvan laatuun verrattuna synteesiin laskostuneilla suunta-arvioilla

Regular sparse array direction of arrival estimation in one dimension

Traditionally regularly spaced antenna arrays follow the spatial Nyquist criterion to guarantee an unambiguous analysis. We present a novel technique that makes use of two sparse non-Nyquist regularly spaced antenna arrays, where one of the arrays is just a shifted version of the other. The method offers several advantages over the use of traditional dense Nyquist spaced arrays, while maintaining a comparable algorithmic complexity for the analysis. Among the advantages we mention: an improved resolution for the same number of receivers and reduced mutual coupling effects between the receivers, both due to the increased separation between the antennas. Because of a shared structured linear system of equations between the two arrays, as a consequence of the shift between the two, the analysis of both is automatically paired, thereby avoiding a computationally expensive matching step as is required in the use of so-called co-prime arrays. In addition, an easy validation step allows to automatically detect the precise number of incoming signals, which is usually considered a difficult issue. At the same time, the validation step improves the accuracy of the retrieved results and eliminates unreliable results in the case of noisy data. The performance of the proposed method is illustrated with respect to the influence of noise as well to the effect of mutual coupling

Online DOA estimation using real eigenbeam ESPRIT with propagation vector matching

International audienceThe Eigenbeam estimation of signal parameters via rotational invariance technique (EB-ESPRIT) [1] is a method to estimate multiple directions-of-arrival (DOAs) of sound sources from a spherical microphone array recording in the spherical harmonics domain (SHD). The method, first, constructs a signal subspace from the SHD signal and then makes use of the fact that, for plane-wave sources, the signal subspace is spanned by the (complex conjugate) spherical harmonic vectors at the source directions. The DOAs are then estimated from the signal subspace using recurrence relations of spherical harmonics.In recent publications, the singularity and ambiguity problems of the original EB-ESPRIT have been solved by jointly combining several types of recurrence relations. The state-of-the-art EB-ESPRIT, denoted as DOA-vector EB-ESPRIT, is based on three recurrence relations [2,3]. This EB-ESPRIT variant can estimate the source DOAs with significantly higher accuracy compared to the other EB-ESPRIT variants [3]. However, a permutation problem arises, which can be solved by using, for example, a joint diagonalization method [3].For parametric spatial audio signal processing purposes in the short-time Fourier transform (STFT) domain, DOA estimates are usually needed per time-frame and frequency bin. In principle, one can use the DOA-vector EB-ESPRIT method to estimate the source DOAs per time-frequency bin in an online manner. However, due to the eigendecompostion of the PSD matrix and the joint diagonalization procedure, the computational cost might be too large for many real-time applications.In this work, we propose a computationally more efficient version of the DOA-vector EB-ESPRIT based on real spherical harmonics recurrence relations. First, we separate the real and imaginary parts of the real SHD signal in the STFT domain and then construct a real signal subspace thereof, which can be recursively estimated using the deflated projection approximation subspace tracking (PASTd) [4] method. For the case of one source per time-frequency bin, the joint diagonalization is not necessary and we can simplify the EB-ESPRIT equations. For the case of two sources, the plane-wave propagation vectors can directly be estimated from the signal subspace eigenvectors by employing properties of the propagation vectors. This method can be seen as a higher order ambisonics extension of the robust B-format DOA estimation in [5]. The proposed method for estimating two DOAs can be summarized as follows:1. Separate real and imaginary parts of the real SHD signal in the STFT domain.2. Recursively estimate the signal subspace eigenvectors using PASTd.3. Estimate the two plane-wave propagation vectors from the signal subspace eigenvectors by using that they span the same subspace and by using properties of the propagation vectors (subspace-propagation vector matching).4. Estimate the DOAs by using three types of real spherical harmonics recurrence relations.Alternatively, one can estimate the DOAs analogously to the complex DOA-vector EB-ESPRIT using the joint diagonalization method proposed in [3].For the evaluation, we simulate SHD signals up to third order with one and two speech sources in reverberant and noisy environments. For the one-source scenarios, we compare the real DOA-vector EB-ESPRIT with subspace estimation based on singular value decomposition (SVD) against PASTd. For the two-source scenarios, we compare the real DOA-vector EB-ESPRIT with joint diagonalization against subspace-propagation vector matching and the robust B-format DOA estimation method.We analyze the angular distributions of the DOA estimates and find, that the DOA estimation using PASTd for the signal subspace estimation is slightly less accurate than the SVD based method but computationally much more efficient. For the estimation of two DOAs, the EB-ESPRIT based methods outperform the robust B-format estimation method when higher SHD orders are considered. The joint diagonalization method is more accurate than the subspace-propagation vector matching method. However, the latter is computationally more efficient.References:[1] H. Teutsch and W. Kellermann, “Detection and localization of multiple wideband acoustic sources based on wavefield decomposition using spherical apertures,” in Proc. IEEE Intl. Conf. Acoust., Speech Signal Proc. (ICASSP), Mar. 2008, pp. 5276–5279.[2] B. Jo and J. W. Choi, “Nonsingular EB-ESPRIT for the localization of early reflections in a room,” J. Acoust. Soc. Am., vol. 144, no. 3, p. 1882, Sep. 2018.[3] A. Herzog and E. A. P. Habets, “Eigenbeam-ESPRIT for DOA-vector estimation,” IEEE Signal Process. Lett., vol. 26, no. 4, pp. 572-576, April 2019.[4] B. Yang – “Projection Approximation Subspace Tracking, IEEE Trans. Sig. Proc.,” vol. 43, no. 1, Jan. 1995.[5] O. Thiergart and E.A.P. Habets, “Robust direction-of-arrival estimation of two simultaneous plane waves from a B-format signal,” IEEE 27th Conv. of Electrical and Electronics Engineers in Israel, Nov. 2012

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Sensor Array Processing with Manifold Uncertainty

<p>The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered. </p><p>First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments. </p><p>As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360$^\circ$ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.</p><p>Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.</p>Dissertatio

Object-based Modeling of Audio for Coding and Source Separation

This thesis studies several data decomposition algorithms for obtaining an object-based representation of an audio signal. The estimation of the representation parameters are coupled with audio-specific criteria, such as the spectral redundancy, sparsity, perceptual relevance and spatial position of sounds. The objective is to obtain an audio signal representation that is composed of meaningful entities called audio objects that reflect the properties of real-world sound objects and events. The estimation of the object-based model is based on magnitude spectrogram redundancy using non-negative matrix factorization with extensions to multichannel and complex-valued data. The benefits of working with object-based audio representations over the conventional time-frequency bin-wise processing are studied. The two main applications of the object-based audio representations proposed in this thesis are spatial audio coding and sound source separation from multichannel microphone array recordings. In the proposed spatial audio coding algorithm, the audio objects are estimated from the multichannel magnitude spectrogram. The audio objects are used for recovering the content of each original channel from a single downmixed signal, using time-frequency filtering. The perceptual relevance of modeling the audio signal is considered in the estimation of the parameters of the object-based model, and the sparsity of the model is utilized in encoding its parameters. Additionally, a quantization of the model parameters is proposed that reflects the perceptual relevance of each quantized element. The proposed object-based spatial audio coding algorithm is evaluated via listening tests and comparing the overall perceptual quality to conventional time-frequency block-wise methods at the same bitrates. The proposed approach is found to produce comparable coding efficiency while providing additional functionality via the object-based coding domain representation, such as the blind separation of the mixture of sound sources in the encoded channels. For the sound source separation from multichannel audio recorded by a microphone array, a method combining an object-based magnitude model and spatial covariance matrix estimation is considered. A direction of arrival-based model for the spatial covariance matrices of the sound sources is proposed. Unlike the conventional approaches, the estimation of the parameters of the proposed spatial covariance matrix model ensures a spatially coherent solution for the spatial parameterization of the sound sources. The separation quality is measured with objective criteria and the proposed method is shown to improve over the state-of-the-art sound source separation methods, with recordings done using a small microphone array