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Active control of noise propagating through apertures is commonly realized with closed-loop LMS algorithms. However, these algorithms require a large number of error microphones and provide only local attenuation. Slow convergence and high computational effort are additional disadvantages. We propose a wave-domain approach that converges instantaneously, operates with low computational effort and does not require error microphones. It inherently controls sound in all directions in the far-field. The soundfield from the aperture is matched in a least squares sense with the generated soundfield from the loudspeaker array using orthonormal basis functions. Compensation for algorithmic delay, induced by blockwise processing, can be based on microphone placement or signal prediction, at the cost of a loss in attenuation performance. Our simulation results indicate that wave-domain processing has the potential to outperform LMS-based methods in practical active noise control for apertures., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems, Team Riccardo Ferrari

We describe a new method to estimate the geometry of a room and reflection coefficients given room impulse responses. The method utilizes convolutional neural networks to estimate the room geometry and multilayer perceptrons to estimate the reflection coefficients. The mean square error is used as the loss function. In contrast to existing methods, we do not require the knowledge of the relative positions of sources and receivers in the room. The method can be used with only a single RIR between one source and one receiver. For simulated environments, the proposed estimation method can achieve an average of 0.04 m accuracy for each dimension in room geometry estimation and 0.09 accuracy in reflection coefficients. For real-world environments, the room geometry estimation method achieves an accuracy of an average of 0.065 m for each dimension., Accepted author manuscript, Circuits and Systems

We describe a new method to estimate the geometry of a room and reflection coefficients given room impulse responses. The method utilizes convolutional neural networks to estimate the room geometry and multilayer perceptrons to estimate the reflection coefficients. The mean square error is used as the loss function. In contrast to existing methods, we do not require the knowledge of the relative positions of sources and receivers in the room. The method can be used with only a single RIR between one source and one receiver. For simulated environments, the proposed estimation method can achieve an average of 0.04 m accuracy for each dimension in room geometry estimation and 0.09 accuracy in reflection coefficients. For real-world environments, the room geometry estimation method achieves an accuracy of an average of 0.065 m for each dimension., Accepted author manuscript, Signal Processing Systems

In this paper, we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method of multipliers, thus, providing insight into its operation. In particular, we show how PDMM combines a lifted dual form in conjunction with Peaceman-Rachford splitting to facilitate distributed optimization in undirected networks. We additionally demonstrate sufficient conditions for primal convergence for strongly convex differentiable functions and strengthen this result for strongly convex functions with Lipschitz continuous gradients by introducing a primal geometric convergence bound., Accepted author manuscript, Circuits and Systems

In this paper, we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method of multipliers, thus, providing insight into its operation. In particular, we show how PDMM combines a lifted dual form in conjunction with Peaceman-Rachford splitting to facilitate distributed optimization in undirected networks. We additionally demonstrate sufficient conditions for primal convergence for strongly convex differentiable functions and strengthen this result for strongly convex functions with Lipschitz continuous gradients by introducing a primal geometric convergence bound., Accepted author manuscript, Signal Processing Systems

Instrumental measures of speech intelligibility typically produce an index between 0 and 1 that is monotonically related to listening test scores. As such, these measures are dimensionless and do not represent physical quantities. In this paper, we propose a new instrumental intelligibility metric that describes speech intelligibility using bits per second. The proposed metric builds upon an existing intelligibility metric that was motivated by information theory. Our main contribution is that we use a statistical model of speech communication that accounts for noise inherent in the speech production process. Experiments show that the proposed metric performs at least as well as existing state-of-the-art intelligibility metrics., (Best student paper award), Circuits and Systems

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality to form equivalent distributed consensus problems. Such problems can be embedded in sensor network applications via existing solvers such as the alternating direction method of multipliers or the primal dual method of multipliers. We then demonstrate how this approach can be used to solve specific problems including linearly constrained quadratic problems and the classic Gaussian channel capacity maximisation problem in a distributed manner., Circuits and Systems

The processing required for the global maximization of the intelligibility of speech acquired by multiple microphones and rendered by a single loudspeaker, is considered in this paper. The intelligibility is quantized, based on the mutual information rate between the message spoken by the talker and the message as interpreted by the listener. We prove that then, in each of a set of narrow-band channels, the processing can be decomposed into a minimum variance distortionless response (MVDR) beamforming operation that reduces the noise in the talker environment, followed by a gain operation that, given the far-end noise and beamforming operation, accounts for the noise at the listener end. Our experiments confirm that both processing steps are necessary for the effective conveyance ofa message and, importantly, that the second step must be aware of the first step., Accepted Author Manuscript, Circuits and Systems

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs., Accepted Author Manuscript, Circuits and Systems

Instrumental measures of speech intelligibility typically produce an index between 0 and 1 that is monotonically related to listening test scores. As such, these measures are dimensionless and do not represent physical quantities. In this paper, we propose a new instrumental intelligibility metric that describes speech intelligibility using bits per second. The proposed metric builds upon an existing intelligibility metric that was motivated by information theory. Our main contribution is that we use a statistical model of speech communication that accounts for noise inherent in the speech production process. Experiments show that the proposed metric performs at least as well as existing state-of-the-art intelligibility metrics., (Best student paper award), Signal Processing Systems

In this paper, we focus on the challenge of processing data generated within decentralised wireless sensor networks in a distributed manner. When the desired operations can be expressed as globally constrained separable convex optimisation problems, we show how we can convert these to extended monotropic programs and exploit Lagrangian duality to form equivalent distributed consensus problems. Such problems can be embedded in sensor network applications via existing solvers such as the alternating direction method of multipliers or the primal dual method of multipliers. We then demonstrate how this approach can be used to solve specific problems including linearly constrained quadratic problems and the classic Gaussian channel capacity maximisation problem in a distributed manner., Signal Processing Systems

The processing required for the global maximization of the intelligibility of speech acquired by multiple microphones and rendered by a single loudspeaker, is considered in this paper. The intelligibility is quantized, based on the mutual information rate between the message spoken by the talker and the message as interpreted by the listener. We prove that then, in each of a set of narrow-band channels, the processing can be decomposed into a minimum variance distortionless response (MVDR) beamforming operation that reduces the noise in the talker environment, followed by a gain operation that, given the far-end noise and beamforming operation, accounts for the noise at the listener end. Our experiments confirm that both processing steps are necessary for the effective conveyance ofa message and, importantly, that the second step must be aware of the first step., Accepted Author Manuscript, Signal Processing Systems

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs., Accepted Author Manuscript, Signal Processing Systems

Electrical Engineering, Mathematics and Computer Science

Electrical Engineering, Mathematics and Computer Science