This paper presents an analysis of spherical microphone array capabilities in the near-field, with an emphasis on radial filtering of sources in a given direction. The near-field of the array is defined in terms of frequency and distance from the array. Directional beamforming is demonstrated given the near-field radial compensation filter, which yields a desired directional beampattern at a chosen distance from the array. This pattern deteriorates as the source draws away from the array. Next, a framework is presented for radial filter design, enabling distance discrimination between sources positioned in the same direction relative to the array. Design examples include Dolph-Chebyshev radial filtering, radial notch filtering, and numerical design. Performance is analyzed in terms of spatial response and robustness to noise. Results show radial filtering is practical for improving attenuation of far-field and near-field interfering sources relative to a desired source positioned in the same direction.

A nearfield spherical microphone array is presented. The nearfield criterion of the spherical array is defined in terms of array order, frequency and location. It is shown that given a source in the nearfield, significant attenuation of farfield interference is achieved. Also, nearfield sources may be attenuated relative to farfield sources. Dereverberation of a nearfield source in a reverberant enclosure is demonstrated using the nearfield microphone array.

In this paper, a novel method for voiced-unvoiced decision within a pitch tracking algorithm is presented. Voiced-unvoiced decision is required for many applications, including modeling for analysis/synthesis, detection of model changes for segmentation purposes and signal characterization for indexing and recognition applications. The proposed method is based on the generalized likelihood ratio test (GLRT) and assumes colored Gaussian noise with unknown covariance. Under voiced hypothesis, a harmonic plus noise model is assumed. The derived method is combined with a maximum a-posteriori probability (MAP) scheme to obtain a pitch and voicing tracking algorithm. The performance of the proposed method is tested using several speech databases for different levels of additive noise and phone speech conditions. Results show that the GLRT is robust to speaker and environmental conditions and performs better than existing algorithms.

In this paper, a novel method for voiced/invoiced decision in speech and music signals is presented. Voiced/unvoiced decision is required for many applications, including better modeling for analysis/synthesis, detection of model changes for segmentation purposes and better signal characterization for indexing and recognition applications. The proposed method is based on the generalized likelihood ratio test (GLRT) and assumes colored Gaussian noise with unknown covariance. Under voiced hypothesis, a harmonic plus noise model is assumed. The derived method is combined with a maximum a-posteriori probability (MAP) scheme to obtain a voiced unvoiced tracking algorithm. The performance of the proposed method is tested under the Keele University database for different signal-to-noise ratios (SNRs), and the results show that the algorithm performs well even under severe noise conditions.