This paper describes a new method for estimating the direction of arrival (DOA) using a nonlinear microphone array system based on complementary beamforming. Complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this system, since the resultant directivity pattern is proportional to the product of these directivity patterns, the proposed method can be used to estimate DOAs of 2(K-1) sound sources with K-element microphone array. First, DOA-estimation experiments are performed using both computer simulation and actual devices in real acoustic environments. The results clarify that DOA estimation for two sound sources can be accomplished by the proposed method with two microphones. Also, by comparing the resolutions of DOA estimation by the proposed method and by the conventional minimum variance method, we can show that the performance of the proposed method is superior to that of the minimum variance method under all reverberant conditions.

This paper describes an improved complementary beamforming microphone array based on the new noise adaptation algorithm. Complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this system, during a pause in the target speech, two directivity patterns of the beamformers are adapted to the noise directions of arrival so that the expectation values of each noise power spectrum are minimized in the array output. Using this technique, we can realize the directional nulls for each noise even when the number of sound sources exceeds that of microphones. To evaluate the effectiveness, speech enhancement experiments and speech recognition experiments are performed based on computer simulations with a two-element array and three sound sources under various noise conditions. In comparison with the conventional adaptive beamformer and the conventional spectral subtraction method cascaded with the adaptive beamformer, it is shown that (1) the proposed array improves the signal-to-noise ratio (SNR) of degraded speech by more than 6 dB when the interfering noise is two speakers with the input SNR of below 0 dB, (2) the proposed array improves the SNR by about 2 dB when the interfering noise is bubble noise, and (3) an improvement in the recognition rate of more than 18% is obtained when the interfering noise is two speakers or two overlapped signals of some speakers under the condition that the input SNR is 10 dB.

This paper describes a spatial spectral subtraction method by using the complementary beamforming microphone array to enhance noisy speech signals for speech recognition. The complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this paper, it is shown that the nonlinear subtraction processing with complementary beamforming can result in a kind of the spectral subtraction without the need for speech pause detection. In addition, the optimization algorithm for the directivity pattern is also described. To evaluate the effectiveness, speech enhancement experiments and speech recognition experiments are performed based on computer simulations under both stationary and nonstationary noise conditions. In comparison with the optimized conventional delay-and-sum (DS) array, it is shown that: (1) the proposed array improves the signal-to-noise ratio (SNR) of degraded speech by about 2 dB and performs more than 20% better in word recognition rates under the conditions that the white Gaussian noise with the input SNR of -5 or -10 dB is used, (2) the proposed array performs more than 5% better in word recognition rates under the nonstationary noise conditions. Also, it is shown that these improvements of the proposed array are same as or superior to those of the conventional spectral subtraction method cascaded with the DS array.