This paper introduces a novel technique for estimating the signal power spectral density to be used in the transfer function of a microphone array post-filter. The technique is a generalization of the existing Zelinski post-filter, which uses the auto- and cross-spectral densities of the array inputs to estimate the signal and noise spectral densities. The Zelinski technique, however, assumes zero cross-correlation between the noise on different sensors. This assumption is inaccurate, particularly at low frequencies and for arrays with closely spaced sensors, and thus the corresponding post-filter is suboptimal in realistic noise conditions. In this paper, a more general expression of the post-filter estimation is developed based on an assumed knowledge of the complex coherence of the noise field. This general expression can be used to construct a more appropriate post-filter in a variety of different noise fields. In experiments using real noise recordings from a computer office, the modified post-filter results in significant improvement in terms of objective speech quality measures and speech recognition performance using a diffuse noise model.

/pdf/microphone-array-post-filter-based-on-noise-field-coherence-8ygqcg54f0.pdf

Microphone array post-filter based on noise field coherence

Researchers in the signal processing community often require sensor signals that result from a spherically or cylindrically isotropic noise field for simulation purposes. Although it has been shown that these signals can be generated using a number of uncorrelated noise sources that are uniformly spaced on a sphere or cylinder, this method is seldom used in practice. In this paper algorithms that generate sensor signals of an arbitrary one- and three-dimensional array that result from a spherically or cylindrically isotropic noise field are developed. Furthermore, the influence of the number of noise sources on the accuracy of the generated sensor signals is investigated.

http://www.eng.biu.ac.il/~gannot/articles/JAS003464.pdf.pdf

Generating sensor signals in isotropic noise fields.

Noise fields encountered in real-life scenarios can often be approximated as spherical or cylindrical noise fields. The characteristics of the noise field can be described by a spatial coherence function. For simulation purposes, researchers in the signal processing community often require sensor signals that exhibit a specific spatial coherence function. In addition, they often require a specific type of noise such as temporally correlated noise, babble speech that comprises a mixture of mutually independent speech fragments, or factory noise. Existing algorithms are unable to generate sensor signals such as babble speech and factory noise observed in an arbitrary noise field. In this paper an efficient algorithm is developed that generates multisensor signals under a predefined spatial coherence constraint. The benefit of the developed algorithm is twofold. Firstly, there are no restrictions on the spatial coherence function. Secondly, to generate M sensor signals the algorithm requires only M mutually independent noise signals. The performance evaluation shows that the developed algorithm is able to generate a more accurate spatial coherence between the generated sensor signals compared to the so-called image method that is frequently used in the signal processing community.

/pdf/generating-nonstationary-multisensor-signals-under-a-spatial-3t1o82rf35.pdf

Generating nonstationary multisensor signals under a spatial coherence constraint.

In this contribution, a dual-channel input-output speech enhancement system is introduced. The proposed algorithm is an adaptation of the well-known superdirective beamformer including postfiltering to the binaural application. In contrast to conventional beamformer processing, the proposed system outputs enhanced stereo signals while preserving the important interaural amplitude and phase differences of the original signal. Instrumental performance evaluations in a real environment with multiple speech sources indicate that the proposed computational efficient spectral weighting system can achieve significant attenuation of speech interferers while maintaining a high speech quality of the target signal.

/pdf/dual-channel-speech-enhancement-by-superdirective-2gwd3jtgjh.pdf

Dual-channel speech enhancement by superdirective beamforming

The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.

Coherent-to-diffuse power ratio estimation for dereverberation

The spatial correlation function between directional microphones is useful in the design and analysis of the performance of these microphones in actual acoustic noise fields. These correlation functions are well known for omnidirectional receivers, but not well known for directional receivers. This chapter investigates the spatial correlation functions for Nth-order differential microphones in both spherically and cylindrically isotropic noise fields. The results are used to calculate the amount of achievable cancellation from an adaptive noise cancellation application using combinations of differential microphones to remove unwanted noise from a desired signal. The results are useful in determining signal-to-noise ratio gains from arbitrarily positioned differential microphone elements in microphone array applications.

Gary W. Elko

Papers

Spatial Coherence Functions for Differential Microphones in Isotropic Noise Fields