Far-field acoustic source localization and bearing estimation using Σ Δ learners

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 57; no. 4; pp. 783 - 792
• Main Authors: Gore, Amit, Fazel, Amin, Chakrabartty, Shantanu
• Format: Journal Article
• Language: English
• Published: IEEE 01-04-2010
• Subjects: Acoustic arrays; Acoustic propagation; Analog-digital conversion; Analog-to-information converter; bearing estimation; Delta-sigma modulation; Direction of arrival estimation; high-dimensional array processing; Microphone arrays; on-chip learning; oversampling converters; Physics; Sigma\Delta modulation; Signal resolution; source localization; Statistical learning; Stochastic processes
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2009.2027627

Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. With sub-wavelength distances between the microphones, resolving acute localization cues become difficult due to precision artifacts. In this paper we propose a framework which overcomes this limitation by integrating signal-measurement (analog-to-digital conversion) with statistical learning (bearing estimation). At the core of the proposed approach is a min-max stochastic optimization of a regularized cost function that embeds manifold learning within ¿¿ modulation. As a result, the algorithm directly produces a quantized sequence of the bearing estimates whose precision can be improved asymptotically similar to a conventional ¿¿ modulators. In this paper we present a hardware implementation of a miniature acoustic source localizer which comprises of: (a) a common-mode canceling microphone array and (b) a ¿¿ integrated circuit which produces bearing parameters. The parameters are then combined in an estimation procedure that can achieve a linear range from 0°-90°. Measured results from a prototype fabricated in a 0.5 ¿m CMOS process demonstrate that the proposed localizer can reliably estimate the bearing of an acoustic source with a resolution less than 2° while consuming less than 75 ¿W of power.