An adaptive differential microphone has been implemented by combining two omnidirectional elements to form back-to-back cardioid directional microphones. By combining the weighted subtraction of these two outputs, any first-order array can be realized. If certain simple constraints are placed on the combination weighting, the null location can be constrained to defined angular regions. Three algorithms that control the constrained adaptation are presented and discussed for the array: the LMS algorithm, Newton’s algorithm, and a time-varying least-squares Wiener filter. A real-time implementation utilizing an AT&T DSP32C digital signal processor is also described.
In a previous talk, ‘‘A new adaptive differential microphone array’’ by Elko and Pong, a differential microphone has been introduced that adapts its directivity pattern to the particular acoustic environment to provide for a good signal-to-noise ratio. There, the selected pattern remains more or less constant with respect to frequency. In this talk an approach is described that contains one more degree of freedom. The spectrum of the signals is partitioned in uniform subbands and different directivity patterns are adaptively chosen in each subband. This allows to cancel multiple noise sources with nonoverlapping spectra. An LMS-based algorithm will be derived with focus on a low computational load and a short delay for the desired signal. Consequences on the speed of adaptation are discussed. Further, experimental results of a first implementation with 33 subbands on a PC-based DSP32C board will be presented. The measurements verify the ability of the algorithm to cancel multiple noise sources with disjoint spectra without distorting the desired signal.
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