Sparse representation of the sound field in a room with dictionary learning

Abstract

Phased array measurements of the sound pressure in a room enable to reconstruct the sound field, i.e., to estimate pressure, velocity and sound intensity in positions that have not been measured. Typically, analytical wave functions are used to expand the measured data and interpolate the wave field. However, these bases are often redundant and lead to non-sparse solutions, as multiple basis functions are required to represent the measured data. In this study, we examine the use of dictionary learning to obtain a sparse representation of the sound field in a room, using atoms learned from experimental data. The aim is to obtain a model of reduced dimensionality that can represent optimally the spatial properties of the sound field in a room. We analyse the properties of the extracted dictionaries, their ability to reconstruct the sound field, and their generality. A broader question is the suitability of a given dictionary, which has been extracted from a particular room, to represent the sound field in another room.

Compressive equivalent source method based on particle velocity measurements for near-field acoustic holography

“…However, this paper mainly focuses on the simple and compact sound source, and the sound source is assumed in the free sound field environment. For the complicated sound source or the spatially distributed sound sources, or the practical non-free sound field, there are still a lot of work to be done, such as the elaborate construction of the sparse basis, [12][13][14] the application the block SBL, 24 or the elimination of the interference sound source or the reflection of the impedance plane with the sound field separation, [26][27][28][29][30]34 when combined with the particle velocity measurement in the future.…”

“…25 Verburg et al and Fernandez-Grande et al applied the CS theory to indoor sound field reconstruction, and obtained good reconstruction results. 26,27 For the complex closed sound field with high frequency, Hahmann et al used the local representations and explored data-driven approaches to obtain suitable models and found that the local partitioning models conform to fields of high spatial complexity. 28 In 2017, Wang and Chen reconstruct the sound field in cylindrical cavity at low frequency by combining sparse regularization with the least square solution of the Helmholtz equation.…”

High-resolution acoustic imaging method based on equivalent source method and reweighted l1 minimization

“…To obtain an acoustic imaging with higher resolution, sparse regularization was applied to NAH on the premise that an appropriate sparse basis of the sound field is constructed. 15,16 At present, the sparse basis is generally obtained by using plane waves, 17 spherical waves, 18 dictionary learning 19,20 et al Among these bases used in NAH, the basis generated by equivalent source method (ESM), that is, free field Green's function has attracted much attention because the prior of the sound field can be achieved in practice and the equivalent source strength obtained by ESM can also be used to realize the acoustic imaging. Sparse regularization-based ESM (S-ESM) was initially proposed to deal with the spatially sparse sources since the corresponding equivalent source strengths are inherently sparse.…”

Spatial reconstruction of sound fields using local and data-driven functions

“…36,37 Parts of this work have been presented at the International Congress on Acoustics ICA 2019 38 and at the 178th Meeting of the Acoustical Society of America 2019. 39…”