Robust Superdirectional Beamforming for Hands-Free Speech Capture in Cars

The application of multichannel signal processing algorithms in front-ends of hands-free speech communication systems facilitates a better extraction of a desired source signal and allows for a suppression of unwanted noise and interference in car interiors. In this paper, a novel superdirective beamforming design method called the Robust Least-Squares Frequency-Invariant Beamformer (RLSFIB) is investigated for microphone arrays in cars. The RLSFIB design offers superior spatial selectivity at low frequencies even for small array apertures and some variability in microphone positioning. To cope with the high sensitivity inherent to superdirectional beamformers, this method directly incorporates a white noise gain (WNG) constraint for controlling the beamformer sensitivity. The resulting problem is formulated as a convex optimization problem which can be solved by conventional iterative solvers and the convexity guarantees a globally optimal solution. As the WNG constraint can be chosen freely, the method can match desired robustness levels, and therefore can be adapted to given prior knowledge on microphone mismatch, positioning errors, and microphone self-noise. Design examples for typical car requirements will be presented. Introduction Interest in the application of multichannel signal processing algorithms in the front-ends of hands-free systems for in-car use has increased in recent years. This interest has been driven primarily by the enactment of regulations which aim to enhance road safety and the demand for convenience. Multichannel signal processing algorithms facilitate a better extraction of a desired speech signal and suppression of unwanted interference signals such as competing speakers and background noise. Beamforming is a class of such multichannel signal processing algorithms. Applications include in-car communication [1], automatic dialog systems for handsfree telephony as well as for more advanced use cases such as interactive navigation systems. Data-independent superdirective beamformers can be used to extract the desired speech signal originating from a chosen passenger within a car while suppressing speech from all other passengers and background noise. Although superdirective beamformers are often desirable because they provide high directivity, a well-known problem inherent in these designs is the high sensitivity to spatially white noise, to the mismatch between microphone characteristics, and to microphone positioning errors [2]. This severely limits their application in practice. Since the WNG is a measure of the robustness of a beamformer, a robust superdirectional beamforming design may be obtained by constraining the WNG [2]. In this paper the RLSFIB (see also [3]), which is a dataindependent frequency-invariant beamforming design, is investigated for microphone arrays in cars. The RLSFIB design directly constrains the WNG to lie above a given lower limit and thereby ensuring a desired level of robustness. This is accomplished by solving a constrained optimization problem which is convex. For simplification of the following treatment, we use the common assumptions that waves propagate in a free field, that the sources are in the farfield relative to the array, and that all microphones are omnidirectional. Beamforming Figure 1 depicts a beamformer with a linear array consisting of N microphones positioned at pn, n = 0, . , N − 1 in space. The microphone signals are first discretized by the analog-to-digital converter (ADC) before being processed by the beamforming filters.