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.

A well used approach for echo cancellation is the two-path method, where two adaptive filters in parallel are utilized. Typically, one filter is continuously updated, and when this filter is considered better adjusted to the echo-path than the other filter, the coefficients of the better adjusted filter is transferred to the other filter. When this transfer should occur is controlled by the transfer logic. This paper proposes transfer logic that is both more robust and more simple to tune, owing to fewer parameters, than the conventional approach. Extensive simulations show the advantages of the proposed method.

When an adaptive filter is used for echo cancellation, it is essential to prevent the filter from diverging in situations when the echo signal is contaminated with near-end disturbance, i.e., during double-talk. This paper presents an extension of a previously proposed double-talk detector for improved performance. It is shown that the computational complexity of the proposed detector is lower than that of the well-used normalized cross correlation (NCC) double-talk detector, at the cost of performance. Further, it is shown that there can be a significant performance difference, in terms of detecting double-talk, between having a fixed echo cancellation filter, which is a common strategy in objective evaluation techniques, and an adaptive filter, which is more close to realistic conditions.

Long delays and sparseness characterize impulse responses in telecommunication networks and a vast number of solutions for network echo cancellation have been proposed over the years. In this paper, an approach for detecting dispersive regions of a sparse impulse response and a proportionate normalized least mean square (PNLMS)-based selective updating approach are combined with an adaptive double-talk detector to form a complete solution for echo cancellation. The proposed solution has low computational complexity and is targeted for systems equipped with external memory.

Parallel adaptive filters have been proposed for echo cancellation to solve the dead-lock problem, occurring when the echo is detected as near-end speech after a severe echo-path change; causing the updating of the adaptive filter to halt. To control the parallel filters and monitor their performance, estimates of the filter deviation (i.e. the squared norm of the filter mismatch vector) are typically used. This paper presents a modification of a filter mismatch estimator. The proposed modification requires slightly more computational resources than the original measure, but provides a significant improvement in terms of robustness during double-talk. This is shown both analytically and through simulations.

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.

This paper introduces the concept of a mapogram. A mapogram may be viewed as a special form of spatiogram, which is a histogram containing additional spatial information. Additionally, this paper presents theory relevant to the creation of a proposed mapogram. A similarity measure derived from the Bhattacharyya coefficient is obtained in order to make comparisons between mapograms. Examples using a mapogram are given.

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.

The purpose of this paper is threefold: firstly, the local successive mean quantization transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up sparse network of winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the receiver operation characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. A Matlab version of the face detection algorithm can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13701& objectType=FILE.

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.

Adaptive filters for echo cancellation generally need update control schemes to avoid divergence in case of significant disturbances. The two-path algorithm avoids the problem of unnecessary halting of the adaptive filter when the control scheme gives an erroneous output. Versions of this algorithm have previously been presented for echo cancellation. This paper presents a transfer logic which improves the convergence speed of the two-path algorithm for acoustic echo cancellation, while retaining the robustness. Results from simulations show an improved performance, and a fixed-point DSP implementation verifies the performance in real-time

A new robust microphone array method to enhance speech signals generated by a moving person in a noisy environment is presented. This blind approach is based on a two-stage scheme. First, a subband time-delay estimation method is used to localize the dominant speech source. The second stage involves speech enhancement, based on the acquired spatial information, by means of a soft-constrained subband beamformer. The novelty of the proposed method involves considering the spatial spreading of the sound source as equivalent to a time-delay spreading, thus, allowing for the estimated intersensor time-delays to be directly used in the beamforming operations. In comparison to previous approaches, this new method requires no special array geometry, knowledge of the array manifold, or acquisition of calibration data to adapt the array weights. Furthermore, such a scheme allows for the beamformer to efficiently adapt to speaker movement. The robustness of the time-delay estimation of speech signals in high noise levels is improved by making use of the non-Gaussian nature of speech trough a subband Kurtosis-weighted structure. Evaluation in a real environment with a moving speaker shows promising results, with suppression levels of up to 16 dB for background noise and interfering (speech) signals, associated to a relatively small effect of speech distortion.

This paper presents a new approach for multiple speaker DOA estimation using an array of microphones. The method relies on the fact that multiple independent speakers have a small overlap in the time-frequency domain, i.e. the individual signals are almost W-disjoint orthogonal. By introducing a time-frequency mask and by continuously tracking the set of time-frequency points corresponding to each individual speech signal, a single source DOA estimation algorithm is used to find the DOA for each separated signal. This approach does not limit the solution to cases where the number of sensors exceeds the number of sources. Real room recordings are used to evaluate the performance of the method where source movements are also included

Today's telecommunications systems use a limited audio signal bandwidth. A typical bandwidth is 0.3-3.4 kHz, but recently it has been suggested that mobile phone networks will facilitate an audio signal bandwidth of 50 Hz-7 kHz. This is suggested since an increased bandwidth will increase the sound quality of the speech signals. Since only few telephones initially will have this facility, a method extending the conventional narrow frequency-band speech signal into a wide-band speech signal utilizing the receiving telephone only is suggested. This will give the impression of a wide-band speech signal. The proposed speech bandwidth extension method is based on models of speech acoustics and fundamentals of human hearing. The extension maps each speech feature separately. Care has been taken to deal with implementation aspects, such as noisy speech signals, speech signal delays, computational complexity, and processing memory usage.

This paper presents the successive mean quantization transform (SMQT). The transform reveals the organization or structure of the data and removes properties such as gain and bias. The transform is described and applied in speech processing and image processing. The SMQT is considered as an extra processing step for the mel frequency cepstral coefficients commonly used in speech recognition. In image processing the transform is applied in automatic image enhancement and dynamic range compression.

This paper explores the successive mean quantization transform (SMQT) for automatic enhancement of gray-scale images. The transform is in the paper presented using set theory. The image enhancement capabilities and properties of the transform are analyzed. The transform is capable to perform both a nonlinear and a shape preserving stretch of the image histogram. Experiments and comparisons to histogram equalization are conducted.

This paper presents a method for least squares design of nonuniform filter banks for application in subband signal processing. Design objectives aim to optimize the filter bank frequency response while minimizing subband and output aliasing. Aliasing is minimized although magnitude and phase changes affect the aliasing terms. Filter banks with increasing bandwidth are designed with the proposed method and evaluated in speech enhancement using a spectral subtraction algorithm. When using a nonuniform frequency resolution approximating that of the human auditory system it is shown that an increased noise reduction and SNR improvement is achieved while maintaining the speech quality for a fixed number of frequency-bands.

This paper presents a new method for the design of oversampled uniform DFT-filter banks for the special application of subband adaptive beamforming with microphone arrays. Since array applications rely on the fact that different source positions give rise to different signal delays, a beamformer alters the phase information of the signals. This in turn leads to signal degradations when perfect reconstruction filter banks are used for the subband decomposition and reconstruction. The objective of the filter bank design is to minimize the magnitude of all aliasing components individually, such that aliasing distortion is minimized although phase alterations occur in the subbands. The proposed method is evaluated in a car hands-free mobile telephony environment and the results show that the proposed method offers better performance regarding suppression levels of disturbing signals and much less distortion to the source speech.

This paper analyzes subband beamforming schemes mainly aimed at speech enhancement and acoustic echo suppression applications such as hands-free telephony for both mobile and office environments, Internet telephony and video conferencing. Analytical descriptions of both causal finite-length and noncausal infinite-length subband microphone array structures are given. More specifically, this paper compares finite Wiener filter performance with the noncausal Wiener solution, giving a comprehensive theoretical suppression limit. It is shown that even short filters will yield a good approximation of the infinite solution, provided that the element spacing and temporal sampling is matched to the frequency band of interest. Typically, 10-20 FIR taps are sufficient in each subband.

This paper presents a method for the design of nonuniform DFT filter banks for subband beamforming. Filter banks designed with the method are evaluated in subband beamforming in a real-world microphone array application. Different source positions in array applications give rise to different signal delays, which means that adaptive beamformers in the subbands alter the phase information of the subband signals in order to extract the source from a noisy background. Phase alterations in the subbands lead to signal degradations when perfect reconstruction filter banks are used for the subband decomposition and reconstruction. The objective of the proposed design is to minimize the magnitude of all aliasing components individually, such that aliasing distortion is minimized although phase alterations occur in the subbands. The proposed method is evaluated in a car hands-free mobile telephony environment with real speech signals and the results show that the performance can be increased by several decibel when using nonuniform filter banks instead of uniform filterbanks while maintaining the length of the subband filters.

In many noise control applications, the noise is dominated by low frequencies and generated by several independent periodic sources. In such situations the tonal noise may be suppressed by using a narrowband multiple-reference feedforward controller. The performance characteristics of the control system, e.g., the convergence behavior and noise reduction are directly related to the controller adaptation rate as well as the frequency separation of the tonal components in the noise, i.e., the beat frequency. This paper treats the convergence performance of a complex least-mean-squares (LMS) algorithm using two reference signals. An analysis of its convergence behavior is presented as well as the results from computer simulations validating the convergence behavior. The convergence of the filter weights and the decrease rate of the squared error (the learning curve) for noise control applications are also discussed.

Subband adaptive filters have been proposed to avoid the drawbacks of slow convergence and high computational complexity associated with time domain adaptive filters. Subband processing introduces transmission delays caused by the filter bank and signal degradations due to aliasing effects. One efficient way to reduce the aliasing effects is to allow a higher sample rate than critically needed in the subbands and thus reduce subband signal degradation. We suggest a design method, for uniform DFT filter banks with any oversampling factor, where the total filter bank group delay may be specified, and where the aliasing and magnitude/phase distortions are minimized.

A common narrow-band speech signal is expanded into a wide-band speech signal. The expanded signal gives the impression of a wide-band speech signal regardless of what type of vocoder is used in a receiver. The robust techniques suggested herein are based on speech acoustics and fundamentals of human hearing. That is the techniques extend the harmonic structure of the speech signal during voiced speech segments and introduce a linearly estimated amount of speech energy in the wide frequency-band. During unvoiced speech segments, a fricated noise may be introduced in the upper frequency-band.

The multimode mean field annealing (MM-MFA) approach is introduced as an efficient tool to design digital filters with discrete coefficients, each implemented as a sum of signed power-of-two terms and additional general hardware constraints. As an application example demonstrating the potential of the new method, we consider the design of a linear phase nonrecursive finite impulse response (FIR) filter with a least squares criterion and minimum number of power-of-two terms.

This paper discusses a multirate filterbank-based extended infomax algorithm for real-world signal separation, i.e., convolved mixtures separation. Since convolution in the time domain corresponds to instantaneous mixing in the frequency domain, polyphase subband projection naturally becomes an efficient alternative to the Fourier transform based frequency domain approach. The online implementation proposed is featured by a simultaneous inverse channel identification in the frequency domain and signal filtering in the time domain. It is shown that an over-representation structure reduces aliasing between different bands and results in more accurate inverse channel estimates. Therefore, it provides better performance than the Fourier transform based structure in the measures of both separation and distortion. The performance limitation of the method is also evaluated in terms of the Wiener solution.

In hands-free speech communication, the signal-to-noise ratio (SNR) is often poor, which makes it difficult to have a relaxed conversation. By using noise suppression, the conversation quality can be improved. This paper describes a noise suppression algorithm based on spectral subtraction. The method employs a noise and speech-dependent gain function for each frequency component. Proper measures have been taken to obtain a corresponding causal filter and also to ensure that the circular convolution originating from fast Fourier transform (FFT) filtering yields a truly linear filtering. A novel method that uses spectrum-dependent adaptive averaging to decrease the variance of the gain function is also presented. The results show a 10-dB background noise reduction for all input SNR situations tested in the range -6 to 16 dB, as well as improvement in speech quality and reduction of noise artifacts as compared with conventional spectral subtraction methods.

This letter presents a new extended active set strategy for optimum finite impulse response (FIR) window design by semi-infinite quadratic programming. The windows may be asymmetric corresponding to frequency responses with general nonlinear phase. The optimality criterion is to minimize the sidelobe energy (L/sub 2/-norm) subject to a peak sidelobe magnitude-constraint (L/sub /spl infin//-constraint). Additional linear constraints are used to form the mainlobe (unity DC gain). Numerical examples involving group delay specifications are used to illustrate the usefulness of the algorithm.

This article presents an evaluation of multiple reference adaptive algorithms. Two least mean squares-types (LMS-types) and a Newton-type algorithm are considered. The special structure of the adaptive filtering problem implies that the Newton-type algorithm can be implemented with the same numerical complexity as LMS-type algorithms. The concept of a first filtered-x Newton algorithm is thus introduced.

This paper gives an analytical description of an adaptive microphone array that facilitates a simple built-in calibration to the environment and instrumentation. This method, suggested for use in hands-free mobile telephones and speech recognition systems for cars, provides speech enhancement and acoustic echo-cancellation. The scheme offers several advantages, such as a simple calibration procedure, suppression of directional sources, versatile robust beamforming, and reduced target signal distortion. The analysis employs noncausal Wiener filters yielding compact and effective theoretical suppression limits.

This paper presents a method to simultaneously perform 20 dB acoustic echo cancellation and 15-20 dB speech enhancement using an adaptive microphone array combined with spectral subtraction. Primarily intended for handsfree telephones in automobiles, the microphone array system simultaneously emphasizes the near-end talker and suppresses the handsfree loudspeaker and the broadband car noise. The array system is based on a fast and efficient on-site calibration and can be used in other situations such as conventional speaker phones.

This paper gives an analytical description of an adaptive microphone array which facilitates a simple built-in calibration to the environment and instrumentation. The scheme offers several advantages, such as a simple calibration procedure and reduced target signal distortion. The analysis employs noncausal Wiener filters yielding compact and effective theoretical suppression limits.

The weighted Chebyshev design of two-dimensional FIR filters is in general not unique since the Haar condition is not generally satisfied. However, for a design based on the discrete frequency domain, the Haar condition might be fulfilled. The question of uniqueness is, however, rather extensive to investigate. It is therefore desirable to define some simple additional constraints to the Chebyshev design in order to obtain a unique solution. The weighted Chebyshev solution of minimum Euclidean filter weight norm is always unique, and represents a sensible additional constraint since it implies minimum white noise amplification. It is shown that this unique Chebyshev solution can always be obtained by using an efficient quadratic programming formulation with a strictly convex objective function and linear constraints. An example where a conventional Chebyshev solution is non-unique is discussed.

A method to solve a general broadband beamformer design problem is formulated as a quadratic program. As a special case, the minimax near-field design problem of a broadband beamformer is solved as a quadratic programming formulation of the weighted Chebyshev approximation problem. The method can also be applied to the design of multidimensional digital FIR filters with an arbitrarily specified amplitude and phase. For linear phase multidimensional digital FIR filters, the quadratic program becomes a linear program. Examples are given that demonstrate the minimax near-field behavior of the beamformers designed.>