Sharpening of Angular Spectra Based on a Directional Re-assignment Approach for Ambisonic Sound-field Visualisation

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A4 Artikkeli konferenssijulkaisussa
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Date

2019-05-01

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Department of Signal Processing and Acoustics

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en

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5

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44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings, pp. 576-580, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

Abstract

A method for computing and sharpening angular spectra, derived from low-order ambisonic signals, is presented in this paper, which is intended for high-resolution directional sound-field visualisation. The method relies on a re-assignment principle, whereby the directional energy for each grid point is assigned to a new direction, which corresponds to a direction-of-arrival (DoA) estimate within a spatially-localised region, centred around the respective grid point. This leads to the concentration of energy around the true sources, and hence, to sharper angular spectra than that of steered response power (SRP) beamformers of maximum directivity, with the same order of ambisonic input. It is demonstrated that the proposed method, when using low-order input, can achieve similar results to the SRP approach of much higher order.

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Keywords

sound-field visualisation, spatial sharpening, Ambisonics, spherical harmonic domain

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DOI

10.1109/ICASSP.2019.8683621

Citation

McCormack, L, Politis, A & Pulkki, V 2019, Sharpening of Angular Spectra Based on a Directional Re-assignment Approach for Ambisonic Sound-field Visualisation. in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings., 8683621, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, pp. 576-580, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8683621

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https://urn.fi/URN:NBN:fi:aalto-201906033324

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