Self-calibrating microphone arrays for speech signal acquisition: A systematic approach

Abstract

The application of microphone arrays and beamforming techniques for speech acquisition promises significant improvement compared to systems operating with a single microphone. Adaptive beamformers offer a potentially superior performance to fixed beamformers particularly in the case of time varying sound field characteristics or in the case of coherent noise such as interfering speakers, loudspeaker signals, etc. However, for real-world applications adaptive beamformers hold the risk of severe signal degradation. Disturbances such as mismatched microphones, an imprecise steering direction or reverberation due to multi-path propagation may cause an adaptive beamformer to distort the desired signal. Microphone mismatch naturally arises from production tolerances as well as from aging effects in the long run. This contribution presents a class of adaptive self-calibration methods. These methods perform a calibration in the background during normal operation of the system and therefore save the need for an additional costly calibration procedure. Based on a systematic approach, new configurations as well as some well-known configurations are derived. The performance of the different self-calibration configurations is examined in a car environment.