Wave Field coding in the spacetime frequency domain

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2011

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This paper presents a signal processing tool for analyzing and manipulating digitized acoustic wave fields, based on a spatio-temporal extension of the time-frequency representation space. The emphasis is on wave fields acquired with a 1-D linear array of equidistant microphones (representing the spatial samples), but the basic formulation is valid for the three dimensions of space. We start by defining a spatio-temporal extension of the short time Fourier transform, in both continuous and discrete space and time, and then focus on applications of the proposed representation. In particular, we show that acoustic scenes with multiple sources can benefit from the use of space-frequency analysis in applications such as source separation (spatial filtering) and spatial audio coding (wave field coding). The experiments suggest that there is a spatial window size for which the performance of filtering and coding is optimal.

Section: Spatial Filtering In the Sstft Domainmentioning

confidence: 99%

Applications of short space-time fourier analysis in digital acoustics

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2011

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“…Denoting , the direct and inverse transforms for each block are given by (32) and (33) Finally, the reconstruction of through overlap-and-add is given by (34) In matrix notation, the complete mechanism of the spatiotemporal MDCT can be expressed as . .…”

Section: Spatio-temporal Lapped Orthogonal Transformsmentioning

confidence: 99%

“…We elaborate on a concept introduced in previous work [32], [33] which consists of compressing the wave field through plane-wave encoding, and obtaining a rate-distortion function at the output.…”

Section: Wave Field Codingmentioning

confidence: 99%

Space-Time-Frequency Processing of Acoustic Wave Fields: Theory, Algorithms, and Applications

IEEE Trans. Signal Process.

2010

Abstract-Consider a nonparametric representation of acoustic wave fields that consists of observing the sound pressure along a straight line or a smooth contour defined in space. The observed data contains implicit information of the surrounding acoustic scene, both in terms of spatial arrangement of the sources and their respective temporal evolution. We show that such data can be effectively analyzed and processed in what we call the space-time-frequency representation space, consisting of a Gabor representation across the spatio-temporal manifold defined by the spatial axis and the temporal axis . In the presence of a source, the spectral patterns generated at have a characteristic triangular shape that changes according to certain parameters, such as the source distance and direction, the number of sources, the concavity of , and the analysis window size. Yet, in general, the wave fronts can be expressed as a function of elementary directional components-most notably, plane waves and far-field components. Furthermore, we address the problem of processing the wave field in discrete space and time, i.e., sampled along and , where a Gabor representation implies that the wave fronts are processed in a block-wise fashion. The key challenge is how to chose and customize a spatio-temporal filter bank such that it exploits the physical properties of the wave field while satisfying strict requirements such as perfect reconstruction, critical sampling, and computational efficiency. We discuss the architecture of such filter banks, and demonstrate their applicability in the context of real applications, such as spatial filtering, deconvolution, and wave field coding.

“…1) conceptually similar to sidelobe canceling except that the processing is performed in what we call the spacetime frequency domain [4], obtained by taking the two-dimensional Fourier transform along the temporal dimension and the spatial dimension (representing the array axis) of the multichannel input. In this domain, the sound acquisition can be directed towards (or null out) a given point in space by performing a 2D sampling operation, where the sampling indexes are determined by the expected spectral pattern generated by a source located at the same point.…”

Section: Introductionmentioning

confidence: 99%

Near-field adaptive beamforming and source localization in the spacetime frequency domain

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

2010

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We revisit the topics of near-field adaptive beamforming and source localization following an alternative approach based on a spatiotemporal spectral representation of the acoustic wave field. With the proposed method, the wave field is expressed as a separable combination of the signal and spatial components that characterize the various sources in the acoustic scene. This allows beamforming operations such as beam steering and sidelobe canceling to be translated into a two-dimensional (2D) sampling problem, where the sampling kernels are derived according to a parametric model representing the 2D spectral pattern generated in the presence of a source. Conversely, the spectral pattern can be estimated from an arbitrary input through the use of parametric spectral estimation techniques, providing a novel solution to the near-field source localization problem.