Spectral Entropy-Based Design of a First-Order Differential Microphone Array

The conventional method of optimizing the parameter of a first-order differential microphone array (DMA) in the presence of noise is to maximize the array gain, i.e., to maximize the noise reduction. Such optimization can be accomplished only in the case of channel noise scenario, where the maximum array gain exists. However, in the case of diffuse or point source noise, the array gain is not upper bounded when the microphones are very closely spaced. Hence, the classical method fails in these two cases. In this paper, we present a method of designing a first-order DMA that ensures effective noise reduction in all the three cases, namely, channel, diffuse, and point-source noise, by utilizing the concept of spectral entropy measure (SEM). Thus, the SEM-based method is superior to the existing maximum array gain method, since it provides the optimal design parameter in all the three noise cases. The performance of the first-order DMA designed using the spectral entropy-based measure is evaluated for an input speech signal in the presence of the three noise scenarios mentioned above. This research underscores the effectiveness of the spectral entropy-based measure in overcoming the limitations posed by the classical method of designing a first-order DMA.