Benny Sällberg

Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data

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.

Source localization for multiple speech sources using low complexity non-parametric source separation and clustering

This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity non-parametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.

Online Maximization of Subband Kurtosis for Blind Adaptive Beamforming in Realtime Speech Extraction

This paper presents a method for blind beamforming with application in realtime speech extraction in a non-stationary environment. The blind beamforming is carried out using an online kurtosis maximization approach where the optimization is based on Newtons method. The main novelty of the paper lies in the formulation of the subband kurtosis approximation, where a locally quadratic criterion is solved at each iteration. Further, a real-time digital signal processor (DSP) implementation of the method is conducted and results with real data is presented.

A mixed analog-digital hybrid for speech enhancement purposes

The paper presents and evaluates a hybrid implementation of a low complexity algorithm for speech enhancement, the adaptive gain equalizer (AGE). The AGE is a subband based time domain method for instantaneous boosting of speech. By a combination of digital analysis and analog synthesis, the main advantages of the digital and analog domains are utilized. The hybrid solution is implemented on a printed circuit board and evaluated in real-time. The development time of the proposed solution was very short, the solution is flexible, robust, has high signal bandwidth in the signal chain and does not require a voice activity detector (VAD). Furthermore, the solution is not restricted by quantization errors in the signal chain and does not require a high speed digital signal processor (DSP) for analysis. Informal listening tests and signal-to-noise ratio (SNR) measures verify excellent speech enhancement performance and quality.

Non-coherent detection of impulse radio UWB signals based on fourth order statistics

Non-coherent receivers are an attractive solution for impulse radio (IR) ultra wideband (UWB) systems due to their low-complexity implementation and low power consumption. However, low-complexity detection is achieved at the expense of some loss in performance. In this paper, a non-coherent detection scheme based on fourth order statistics, i.e., kurtosis, is proposed for low data rate IR-UWB systems. The performance of a conventional energy detector (ED) and the proposed kurtosis detector (KD) is compared in terms of bit-error-rate (BER). The simulation results validate that the KD receiver outperforms a conventional ED receiver on both additive white Gaussian noise (AWGN) and IEEE 802.15.4a multipath channels.

Robust Weighted Non-Coherent Receiver for Impulse Radio UWB PPM Signals

This letter proposes an energy detection based robust weight estimation scheme for pulse-position modulated (PPM) impulse radio ultra-wideband (IR-UWB) signals using weighted non-coherent receiver (WNCR). Conventional data-aided WNCR (DA-WNCR) scheme estimates the weighting coefficients, or channel state information (CSI), using a large number of training symbols over time-varying channels. In contrast, the proposed Robust WNCR (R-WNCR) scheme is non-data-aided (NDA), adaptive and robust to channel variations. The proposed R-WNCR estimates the weighting coefficients adaptively based on the received stochastic data, and the weight estimation process is refined using a decision directed approach.

Real-Time DSP Implementation of a Subband Beamforming Algorithm for Dual Microphone Speech Enhancement

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.

Analog circuit implementation for speech enhancement purposes

Human speech is the main method for personal communication. However, interfering noise could degrade the intelligibility of speech, eventually resulting in errors. Thus, efficient speech enhancement algorithms are needed for example in hand held battery powered hearing aids. This paper presents an implementation of a time domain method for speech enhancement purposes: the adaptive gain equalizer. The implementation is carried out on a printed circuit board using common analog electronic components, and evaluated in real-time. The proposed solution benefits from high system bandwidth, it neither quantizes nor digitalizes data, and it is likely to have more efficient power consumption as opposed to many digital signal processor (DSP) based solutions. The evaluation proves the speech enhancement performance of the analog circuit implementation.

Modulation Domain Adaptive Gain Equalizer for Speech Enhancement

This paper evaluates speech enhancement by filtering in the modulation frequency domain, as an alternative to filtering in conventional frequency domain. Adaptive Gain Equalizer (AGE) is a commonly ...

Complex-Valued Independent Component Analysis for Online Blind Speech Extraction

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 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.