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

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.