Joint source localization and dereverberation by sound field interpolation using sparse regularization

In this paper, source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists in the interpolation of the sound field measured by a set of microphones by matching the recorded sound pressure with that of a particular acoustic model. This model is based on a collection of equivalent sources creating either spherical or plane waves. In order to achieve meaningful results, spatial, spatio-temporal and spatio-spectral sparsity can be promoted in the signals originating from the equivalent sources. The inverse problem consists of a large-scale optimization problem that is solved using a first order matrix-free optimization algorithm. It is shown that once the equivalent source signals capable of effectively interpolating the sound field are obtained, they can be readily used to localize a speech sound source in terms of Direction of Arrival (DOA) and to perform dereverberation in a highly reverberant environment

Interpolation and range extrapolation of sound source directivity based on a spherical wave propagation model

Approaches for incorporating sound source directivity into wave-based room acoustic simulations using a spherical harmonic representation have been presented recently. Normally, the directivity is measured or prescribed on a spherical surface centered at the nominal source position. In wave-based simulations, this directivity can be represented through a locally-defined driving term acting at the source location. In practice, the directivity of real-world sound sources like musical instruments or industrial machinery can only be measured approximately in terms of spatial resolution and accuracy. We show that the measurement data can be augmented such that the impairments due to the limitations of the measurement accuracy are mitigated. We revisit the previously proposed approach of only using the angle-dependent magnitude of the measured directivity together with a spherical-wave propagation model and demonstrate its potential by means of numerical simulations based on two case studies

Impulse Response Interpolation via Optimal Transport

Interpolation between multiple room impulse responses is often necessary for dynamic auralization of virtual acoustic environments, in which a listener can move with six degrees-of-freedom. The spatial room impulse response (SRIR) represents the combined effects of the surround room as sound propagates from a source to the listener and varies as the source or listener positions change. The early portion of the SRIR contains sparse reflections, considered to be distinct sound events, that tend to be impaired with interpolation methods based on simple linear combinations. With parametric processing of SRIRs, corresponding sound events are able to be mapped to one another and produce a more physically accurate spatiotemporal interpolation of the early portion of the SRIR. In this thesis, a novel method for parametric SRIR interpolation is proposed based on the principle of optimal transportation. First, SRIRs are represented as point clouds of sound pressure in a 3D virtual source space. Mappings between two point clouds are obtained by defining a partial optimal transport problem problem, solvable with familiar linear programming techniques. The partial relaxation is implemented by permitting both point-to-point mappings and dummy mappings. The obtained optimal transport plan is used to compute the interpolated point cloud which is converted back to an SRIR. Testing of the proposed method against three baseline comparison methods was done with SRIRs generated by geometrical acoustical modeling. An error metric based on the difference in energy between low-passed rendering of the omnidirectional room impulse response was used. Statistical results indicate that the proposed method consistently outperforms the baseline methods of interpolation. Qualitative examination of the mapping methods confirms that partial transport produces more physically accurate spatiotemporal mappings. For future work, it is suggested to consider different cost functions, interpolate between measured SRIRs, and to render the responses to allow perceptual tests

Incorporating source directivity in wave-based virtual acoustics:Time-domain models and fitting to measured data

The modeling of source directivity is a problem of longstanding interest in virtual acoustics and auralisation. This remains the case for newer time domain volumetric wave-based approaches to simulation such as the finite difference time domain method. In this article, a spatio-temporal model of acoustic wave propagation, including a source term is presented. The source is modeled as a spatial Dirac delta function under the action of a series of differential operators associated with the spherical harmonic functions. Each term in the series gives rise to the directivity pattern of a given spherical harmonic, and is separately driven through a time domain filtering operation of an underlying source signal. Such a model is suitable for calibration against measured frequency-dependent directivity patterns and a procedure for arriving at time domain filters for each spherical harmonic channel is illustrated. It also yields a convenient framework for discretisation, and a simple strategy is presented, yielding a locally-defined operation over the spatial grid. Numerical results, illustrating various features of source directivity, including the comparison of measured and synthetic directivity patterns, are presented

Deep Sound Field Reconstruction in Real Rooms:Introducing the ISOBEL Sound Field Dataset

Knowledge of loudspeaker responses are useful in a number of applications, where a sound system is located inside a room that alters the listening experience depending on position within the room. Acquisition of sound fields for sound sources located in reverberant rooms can be achieved through labor intensive measurements of impulse response functions covering the room, or alternatively by means of reconstruction methods which can potentially require significantly fewer measurements. This paper extends evaluations of sound field reconstruction at low frequencies by introducing a dataset with measurements from four real rooms. The ISOBEL Sound Field dataset is publicly available, and aims to bridge the gap between synthetic and real-world sound fields in rectangular rooms. Moreover, the paper advances on a recent deep learning-based method for sound field reconstruction using a very low number of microphones, and proposes an approach for modeling both magnitude and phase response in a U-Net-like neural network architecture. The complex-valued sound field reconstruction demonstrates that the estimated room transfer functions are of high enough accuracy to allow for personalized sound zones with contrast ratios comparable to ideal room transfer functions using 15 microphones below 150 Hz