The proposed DOA (Direction Of Arrival) estimation method by integrating the frequency array data generated from microphone pairs in an equilateral-triangular microphone array is extended here. The method uses four microphones located at the apices of regular tetrahedron to enable to estimate the elevation angle from the array plane as well. Furthermore, we introduce an idea for separate estimation of azimuth and elevation to reduce the computational loads.

In this paper, we propose a DOA (Direction Of Arrival) estimation method of speech signal using three microphones. The angular resolution of the method is almost uniform with respect to DOA. Our previous DOA estimation method using the frequency-domain array data for a pair of microphones achieves high precision estimation. However, its resolution degrades as the propagating direction being apart from the array broadside. In the method presented here, we utilize three microphones located at vertices of equilateral triangle and integrate the frequency-domain array data for three pairs of microphones. For the estimation scheme, the subspace analysis for the integrated frequency array data is proposed. Through both computer simulations and experiments in a real acoustical environment, we show the efficiency of the proposed method.

In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.