This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases.

This letter aims at complementing previous empirical work regarding a certain beamforming technique for blind speech extraction that uses a local quadratic approximation of a Kurtosis expression. It is shown here that the proposed method possesses a fixed-point property which means that it remains at an optimal solution once this solution has been reached. The proposed method's fixed-point property is valid for a range of source signals including Gaussian sources. This is an improvement over the FastICA method which diverges at the optimal points that correspond to a Gaussian source. In a real application, it cannot be assured that non-Gaussian mixtures are constantly observed; hence, the proposed method is a viable alternative in that case. The fixed-point property further implies that the approximative Kurtosis expression is identical to the true Kurtosis value at an optimal point which, in turn, means that the approximation error is zero. In addition, the convergence towards an optimal solution is always in the direction of a local minimum point even though the optimal solution that correspond to a super-Gaussian source is always a maximum solution which harmonizes with the concept of Kurtosis maximization.

The performance of single-channel temporal noise reduction methods generally deteriorate in high noise environments, whereas spatial beamformers can maintain some level of speech enhancement. This paper presents a solution where a low complexity single-channel noise reduction method is integrated into the feedback control loop of an adaptive blind beamformer with the purpose of robust blind speech extraction in high noise environments. The proposed combined system outperforms each of the individual methods with respect to signal-to-interference ratio improvement for a wide range of operating conditions, and where the loss in estimated perceptual speech quality due to the combined system is tolerably low. Furthermore, the excess processing load in a hardware solution is comparatively insignificant for the proposed extended approach.

In many applications where speech separation and enhancement is of interest, e.g. conferencing systems, mobile phones and hearing aids, accurate speaker localization is important. This paper presents an alternative criteria for the well known steered response power with phase transform (SRP-PHAT) algorithm, in which the steered response relates to peaks in the fourth order cross cumulant, rather than peaks in the second order cross cumulant, i.e. the cross power spectrum. Since speech sources have a probability density function (PDF) close to the Laplacian distribution and noise are generally closer to the Gaussian distribution, the fourth order cumulant becomes a good alternative for the steered response search for speech sources. The proposed method is evaluated and compared to the original SRP-PHAT algorithm and shows significant improvements in localization performance for speech sources.

This paper presents a theoretical analysis of a certain criterion for complex-valued independent component analysis (ICA) with a focus on blind speech extraction (BSE) of a spatio–temporally nonstationary speech source. In the paper, the proposed criteria denoted KSICA is related to the well-known FastICA method with the Kurtosis contrast function. The proposed method is shown to share the important fixed-point feature with the FastICA method, although an improvement with the proposed method is that it does not exhibit the divergent behavior for a mixture of Gaussian-only sources that the FastICA method tends to do, and it shows better performance in online implementations. Compared to the FastICA, the KSICA method provides a 10 dB higher source extraction performance and a 10 dB lower standard deviation in a data batch approach when the data batch size is less than 100 samples. For larger batch sizes, the KSICA metod performs equally well. In an online application with spatially stationary sources the KSICA method provides around 10 dB higher interference suppression, and 1 MOS-unit lower speech distortion compared to the FastICA for 0.15 s time constant in the algorithm update parameter. Thus, the FastICA performance matches the KSICA performance for a time constant above 1 s. Finally, in an online application with a moving speech source, the KSICA method provides 10 dB higher interference suppression, compared to the FastICA for the same algorithm settings. All in all, the proposed KSICA method is shown to be a viable alternative for online BSE of complex-valued signal mixtures.

A real-time digital signal processor (DSP) based implementation of a subband beamforming algorithm and its evaluation for dual microphone speech enhancement is presented. The algorithm, a calibrated constrained beamformer, is described theoretically and a real-time structure is proposed, including an efficient approach for multichannel data transformation. Measurements show that the battery driven DSP implementation supports 20 h operation-time, with an improved signal-to-noise ratio (SNR) of up to 14 dB in high-noise factory environment. Further, less than half the provided computational performance of the DSP is used by the proposed method, hence, processing of additional tasks may be included.