In the field of human speech capturing systems, a fundamental role is played by the source localization algorithms. In this paper a Speaker Localization algorithm (SLOC) based on Deep Neural Networks (DNN) is evaluated and compared with state-of-the art approaches. The speaker position in the room under analysis is directly determined by the DNN, leading the proposed algorithm to be fully data-driven. Two different neural network architectures are investigated: the Multi Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). GCC-PHAT (Generalized Cross Correlation-PHAse Transform) Patterns, computed from the audio signals captured by the microphone are used as input features for the DNN. In particular, a multi-room case study is dealt with, where the acoustic scene of each room is influenced by sounds emitted in the other rooms. The algorithm is tested by means of the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room. In detail, the focus goes to speaker localization task in two distinct neighboring rooms. As term of comparison, two algorithms proposed in literature for the addressed applicative context are evaluated, the Crosspower Spectrum Phase Speaker Localization (CSP-SLOC) and the Steered Response Power using the Phase Transform speaker localization (SRP-SLOC). Besides providing an extensive analysis of the proposed method, the article shows how DNN-based algorithm significantly outperforms the state-of-the-art approaches evaluated on the DIRHA dataset, providing an average localization error, expressed in terms of Root Mean Square Error (RMSE), equal to 324 mm and 367 mm, respectively, for the Simulated and the Real subsets.

Novelty detection is the task of recognising events the differ from a model of normality. This paper proposes an acoustic novelty detector based on neural networks trained with an adversarial training strategy. The proposed approach is composed of a feature extraction stage that calculates Log-Mel spectral features from the input signal. Then, an autoencoder network, trained on a corpus of “normal” acoustic signals, is employed to detect whether a segment contains an abnormal event or not. A novelty is detected if the Euclidean distance between the input and the output of the autoencoder exceeds a certain threshold. The innovative contribution of the proposed approach resides in the training procedure of the autoencoder network: instead of using the conventional training procedure that minimises only the Minimum Mean Squared Error loss function, here we adopt an adversarial strategy, where a discriminator network is trained to distinguish between the output of the autoencoder and data sampled from the training corpus. The autoencoder, then, is trained also by using the binary cross-entropy loss calculated at the output of the discriminator network. The performance of the algorithm has been assessed on a corpus derived from the PASCAL CHiME dataset. The results showed that the proposed approach provides a relative performance improvement equal to 0.26% compared to the standard autoencoder. The significance of the improvement has been evaluated with a one-tailed z-test and resulted significant with p <; 0.001. The presented approach thus showed promising results on this task and it could be extended as a general training strategy for autoencoders if confirmed by additional experiments.

Among the many electrical load disaggregation methods, often referred to as Non-Intrusive Load Monitoring techniques, the Additive Factorial Approximate MAP (AFAMAP) algorithm has shown outstanding capabilities and, therefore, it is nowadays regarded as a reference model. In order to achieve more accurate disaggregation results, and to satisfy real life environment requirements, further improvements in the algorithm are needed. In this work, the AFAMAP algorithm has been extended, by means of a differential forward model, thus complementing the existing differential backward model. Furthermore, an aggregated data examination method has been employed, aimed to the detection of inadmissible working state combinations of appliances, as well as the constraints setting based on the reactive power disaggregation feedback. The new approach has been evaluated by means of a subset, spanning over 6 months, of the Almanac of Minutely Power dataset (AMPds). On purpose, a real life environment, accounting 6 appliances, has been modelled and the carried out experiments revealed a improvement up to 18% with respect to the baseline AFAMAP.

In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds). New experiments are performed, as well, to evaluate the results of the application of both temporal features and pressure data. The pressure data is obtained by means of the EPANEt tool, whereas the leakages are induced at run-time for a more realistic behaviour. Two alternative sets of temporal features are evaluated by combining them with the features extracted from both flow and pressure data. Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and One-Class Support Vector Machine (OC-SVM) are used to characterize the normal data behaviour, under a comparative perspective. A feature selection strategy is adopted in computer simulations and the resulting performance indices are evaluated in terms of Area Under Curve (AUC). The obtained results show that the introduction of the temporal information produces a slight performance improvement for both flow and pressure data, but, most importantly, the combination of flow and pressure features allows a significant improvement of leakage detection for both GMM and HMM at every resolution, up to 88% of AUC.

Compression driver design involves the study of complex mathematical models characterized by a great number of variables, implying high computational cost and long design time. Therefore, an optimization procedure is required to enhance the design procedure, especially from the parameters point of view. In this paper, a combined approach based both on evolution strategy procedure and neural network model is presented. Taking into consideration several tests on a real compression driver, the proposed method is capable to enhance the design procedure from the point of view of obtained frequency response and of the computational performance.

This paper focuses on Voice Activity Detectors (VAD) for multi-room domestic scenarios based on deep neural network architectures. Interesting advancements are observed with respect to a previous work. A comparative and extensive analysis is lead among four different neural networks (NN). In particular, we exploit Deep Belief Network (DBN), Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory recurrent neural network (BLSTM) and Convolutional Neural Network (CNN). The latter has recently encountered a large success in the computational audio processing field and it has been successfully employed in our task. Two home recorded datasets are used in order to approximate real-life scenarios. They contain audio files from several microphones arranged in various rooms, from whom six features are extracted and used as input for the deep neural classifiers. The output stage has been redesigned compared to the previous author's contribution, in order to take advantage of the networks discriminative ability. Our study is composed by a multi-stage analysis focusing on the selection of the features, the network size and the input microphones. Results are evaluated in terms of Speech Activity Detection error rate (SAD). As result, a best SAD equal to 5.8% and 2.6% is reached respectively in the two considered datasets. In addiction, a significant solidity in terms of microphone positioning is observed in the case of CNN.

In this paper we introduce a fully flexible coarse-grained model of immunoglobulin G (IgG) antibodies parametrized directly on cryo-EM data and simulate the binding dynamics of many IgGs to antigens adsorbed on a surface at increasing densities. Moreover, we work out a theoretical model that allows to explain all the features observed in the simulations. Our combined computational and theoretical framework is in excellent agreement with surface-plasmon resonance data and allows us to establish a number of important results. (i) Internal flexibility is key to maximize bivalent binding, flexible IgGs being able to explore the surface with their second arm in search for an available hapten. This is made clear by the strongly reduced ability to bind with both arms displayed by artificial IgGs designed to rigidly keep a prescribed shape. (ii) The large size of IgGs is instrumental to keep neighboring molecules at a certain distance (surface repulsion), which essentially makes antigens within reach of the second Fab always unoccupied on average. (iii) One needs to account independently for the thermodynamic and geometric factors that regulate the binding equilibrium. The key geometrical parameters, besides excluded-volume repulsion, describe the screening of free haptens by neighboring bound antibodies. We prove that the thermodynamic parameters govern the low-antigen-concentration regime, while the surface screening and repulsion only affect the binding at high hapten densities. Importantly, we prove that screening effects are concealed in relative measures, such as the fraction of bivalently bound antibodies. Overall, our model provides a valuable, accurate theoretical paradigm beyond existing frameworks to interpret experimental profiles of antibodies binding to multi-valent surfaces of different sorts in many contexts.

Multichannel acoustic echo cancellers aim at reducing the undesired acoustic coupling among microphones and loudspeakers in multichannel audio/video teleconferencing systems. In this scenario, double-talk refers to the situation in which there is an active speaker both in the remote room and in the local room. The detection of double-talk is one of the main issues to be faced in such systems to ensure a correct echo cancellation. Indeed, filters coefficients update has to be suppressed during double-talk in order to prevent the filter from diverging because of the presence of the local speaker. In this paper, a novel approach for double-talk detection is discussed based on the estimation of the fundamental frequency of the residual error signals through adaptive second-order notch filters. More specifically, simultaneous speaker activity in the remote and local rooms is revealed by exploiting the same frequency tracking introduced for multichannel decorrelation. Several experiments have been carried out to prove the effectiveness of the proposed approach also making comparison with other solutions of the state of the art.

In this paper, a novelty detection algorithm for the identification of leakages in smart water/gas grid contexts is proposed. It is based on two separate stages: the first deals with the creation of the statistical leakage-free model, whereas the second evaluates the eventual occurrence of leakage on the basis of the model likelihood. Up to the authors' knowledge, this approach has never been used in the application scenario of interest. A set of several features are extracted from the Almanac of Minutely Power Dataset, and a suboptimal selection is executed to determinate the best combination. The abnormal event (leakage) is induced by manipulating the consumption in the test set. A total of 10 background models are created, by employing both Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) under a comparative perspective, and each of them is adopted to detect 10 leakages, with random duration, length and starting time. Finally, the performance are evaluated in terms of Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC). Obtained results are more than encouraging: the best average AUCs of 85.60% and 87.97% are achieved with HMM, at 1 minute resolution, for natural gas and water, respectively. Specifically, considering true detection rates (TDRs) of 100%, the natural gas exhibits an overall false detection rate (FDR) of 17.11%, and the water achieves an overall FDR of 13.79%.

This paper presents a Voice Activity Detector (VAD) for multi-room domestic scenarios. A multi-room VAD (mVAD) simultaneously detects the time boundaries of a speech segment and determines the room where it was generated. The proposed approach is fully data-driven and is based on a Deep Neural Network (DNN) pre-trained as a Deep Belief Network (DBN) and fine-tuned by a standard error back-propagation method. Six different types of feature sets are extracted and combined from multiple microphone signals in order to perform the classification. The proposed DBN-DNN multi-room VAD (simply referred to as DBN-mVAD) is compared to other two NN based mVADs: a Multi-Layer Perceptron (MLP-mVAD) and a Bidirectional Long Short-Term Memory recurrent neural network (BLSTM-mVAD). A large multi-microphone dataset, recorded in a home, is used to assess the performance through a multi-stage analysis strategy comprising multiple feature selection stages alternated by network size and input microphones selections. The proposed approach notably outperforms the alternative algorithms in the first feature selection stage and in the network selection one. In terms of area under precision-recall curve (AUC), the absolute increment respect to the BLST-mVAD is 5.55%, while respect to the MLP-mVAD is 2.65%. Hence, solely the proposed approach undergoes the remaining selection stages. In particular, the DBN-mVAD achieves significant improvements: in terms of AUC and F-measure the absolute increments are equal to 10.41% and 8.56% with respect to the first stage of DBN-mVAD.

Acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes due to coupling between microphones and loudspeakers. Stereophonic systems provide more realistic experience than single-channel systems, since listeners have spatial information that helps to identify the speaker position. Assuming this scenario, a suitable choice for the system parameters becomes essential to improve the audio reproduction quality. Error-driven optimization strategies are usually used to obtain an optimal system configuration but there is no relationship with the quality desired by the user. In this paper, an interactive evolutionary algorithm is adopted for a stereophonic acoustic echo cancellation system in order to meet subjective specifications in the optimization stage. In this way, the optimal system configuration is derived according to a user-driven approach in order to satisfy the quality requirements demanded by users availing such stereophonic systems. Experimental results prove the effectiveness of the proposed interactive architecture according to both objective and subjective measures.

Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of technologies, available at demand side, a proper energy manager with the support of a pricing profile forecaster is mandatory. Although energy management and price forecasting are recurrent topics, in literature they have been addressed separately. On the other hand, in this work, the aim is to investigate how well an energy manager is able to perform in presence of data uncertainty originating from the forecasting process. On purpose, an energy and resource manager has been revised and extended in the current manuscript. Finally, it has been complemented with a price forecasting technique, based on the Extreme Learning Machine paradigm. The proposed forecaster has proven to be better performing and more robust, with respect to the most common forecasting approaches. The energy manager, as well, has proven that the energy efficiency of the residential environment can be improved significantly. Nonetheless, to achieve the theoretical optimum, forecasting techniques tailored for that purpose may be required.

In the field of immersive audio, crosstalk cancellation is adopted preserving the spatial characteristics of the reproduced signals. It is based on the use of HRTFs that can be approximated through a free-field model. However, this method assumes that the physical head is removed and replaced with two monopole receivers at the position of the ears and thus the source characteristics is not considered. In this context, a new approximation for the HRTFs is introduced taking into account the source directivity, i.e., the loudspeaker is modelled as a rigid sphere with a membrane vibrating on a spherical cap. A deep investigation on the effect of introducing the source directivity in the free-field model is reported in this paper. In particular, several experimental results are shown, considering both objective and subjective measurements, to validate the effectiveness of the proposed solution in terms of both channel separation and audio quality preservation.

Voice activity detection algorithms are intended to discriminate vocal segments from silence or other background noise periods. This is an important aspect in many speech communication systems and applications, e.g. teleconferencing systems. Therefore, a new method for detecting voiced segments in a multichannel scenario is proposed in this work by exploiting the fundamental frequency tracking through adaptive notch filters previously introduced for interchannel coherence reduction in multichannel acoustic echo cancellation systems. Novel expedients are presented in this paper for obtaining a more accurate tracking and thus for guaranteeing a correct voiced/unvoiced decision. The great advantage of the presented solution is related to the possibility of deriving a self-contained solution for multichannel acoustic echo cancellation where all the involved algorithms are based on the same fundamental frequency estimation. Experimental results prove the effectiveness of the proposed approach in correctly identifying vocal segments and silence and background noise periods.

Immersive speech communication systems have been gaining increasing attention due to their ability to reproduce enhanced acoustic images, and thus achieving good performance in terms of sound quality and accuracy. In this context , a fundamental role is played by intelligent acoustic interfaces (IAIs), which aim at acquiring and/or reproducing desired acoustic information with enhanced perception. The recent widespread availability of multimedia devices, equipped with different kind of sensors, has broadened the range of data processing methods, thus giving a chance for developing advanced IAIs. In this paper, we propose an immersive communication system composed of two IAIs: the first one exploits microphones and cameras, together with a signal processing system, to reduce unwanted noise and enhance the speech quality of the desired information in the transmitting room; the second one is an advanced reproduction system based on a loudspeaker array and on an effective wave field synthesis technique capable of reproducing the spatial perception of the desired speech source in the receiving room. The whole system has been assessed in simulated and real immersive communication scenarios: objective and subjective evaluations have been shown the effectiveness of the proposed system.

Multichannel sound reproduction systems aim at providing an optimal acoustical sensation to the listener by enhancing the listening experience and improving the spatial sound impression of the virtual scene. In this context, many audio applications requiring adaptive processing schemes have been developed in order to improve the performance of the audio reproduction. However, online multichannel systems identification requires the introduction of suitable solutions for overcoming the well-known non-uniqueness problem related to the correlation existing among the loudspeaker signals. This issue has been deeply investigated considering stereophonic systems but its extension to multichannel systems may not be so straightforward. In this paper, a novel solution for speech and audio signals is presented based on the “missing-fundamental” phenomenon. Experimental results proved the effectiveness of the approach also making comparisons with the existing state of the art.

The paper deals with the development of a real time system for the reproduction of an immersive audio field considering the crosstalk cancellation and the room response equalization issues. In particular, the real-time system is composed of two parts: a crosstalk cancellation network and a combined multipoint equalization structure. The former is required in order to have a spatialized audio and it is based on the free-field relationship in order to not introduce a timbre alteration. The latter is used to improve the objective and subjective quality of sound reproduction systems by compensating the room and loudspeakers transfer function. Both steps are based on a-priori analysis of the real environment using real impulse responses measured in different positions. In particular, an offline procedure capable of determining the tuning parameters for the crosstalk network and of deriving the final filters for the equalization structure, is adopted. Several results are presented in order to show the effectiveness of the proposed algorithms considering objective and subjective evaluations and comparing the presented approach with the state of the art.

Nowadays, there is a large interest towards multimedia audio systems as a consequence of the development of advanced digital signal processing techniques. In particular, immersive speech communication system has gaining increasing attention since they allow to reproduce realistic acoustic image, and thus achieving good performance in terms of sound quality and accuracy. In this scenario a fundamental role is played by intelligent acoustic interfaces which aim at acquiring audio information, processing it, and returning the processed information to the audio rendering system. In this paper, an effective intelligent acoustic interface composed of a microphone array and a signal processing system capable to enhance the intelligibility of the desired information of the transmitting room is proposed, combined with an advanced reproduction system based on an efficient application of a wave field synthesis technique capable to reproduce an immersive scenario in the receiving room. The whole system has been assessed within a speech communication application involving a moving desired source in a real scenario: objective and subjective evaluation has been reported in order to show the overall system performance.

Template-matching and discriminative techniques, like support vector machines (SVMs), have been widely used for automatic speech recognition. Both methods require that varying length sequences are mapped to vectors of fixed lengths: in template-matching, the problem is solved by means of dynamic time warping (DTW), while in SVM with dynamic kernels. The supervector and i-vector paradigms seem to represent a valid solution to such a problem when SVM are employed for classification. In this work, Gaussian mean supervectors (GMS), Gaussian posterior probability supervectors (GPPS) and i-vectors are evaluated as features both for template-matching and for SVM-based speech recognition in a comparative fashion. All these features are based on Power Normalized Cepstral Coefficients (PNCCs) directly extracted from speech utterances. The different methods are assessed in small vocabulary speech recognition tasks using two distinct corpora, and they have been compared to DTW, dynamic time alignment kernel (DTAK), outerproduct of trajectory matrix, and PocketSphinx as further recognition techniques to be evaluated. Experimental results showed the appropriateness of the supervector and i-vector based solutions with respect to the other state-of-the art techniques here addressed.

Within micro grid scenarios, optimal energy management represents an important paradigm to improve the grid efficiency while lowering its burden. While usually real time energy management is considered, an offline approach can be also adopted to maximize the grid efficiency in certain contexts. Indeed, by evaluating the energy management performance according to the user needs, it is possible to asses which technologies allow the overall system to operate at its best, given the expected load level. From this perspective, a computational framework based on the "Mixed-Integer Linear Programming" paradigm has been proposed in this paper as a tool to simulate the micro grid behaviour in terms of energy consumption and in dependence on the technology of choice. By modelling the energy production and storage means, the pool of electricity tasks, and the thermal behaviour of the building, suitable energy management policies for the micro grid scenario under study can be developed and tested in different operating conditions and time horizons. Moreover, the forecasting paradigm has been integrated into the framework to deal with data uncertainty, and a Neural Network approach has been employed on purpose. Performed computer simulations, related to a six-apartments building scenario, have proven that the suggested framework can fruitfully be adopted to assess the effectiveness of different technical solutions in terms of overall energy cost, thus supporting the decisional process occurring during the micro grid design.

Reverberation is a well known effect that has an important role in our listening experience. A great deal of research has been devoted in the last decades aiming to artificially reproduce the reverberation effect exploiting a hybrid reverberation structure. In this context, several automatic procedures have been presented in the literature in order to derive the reverberator structure considering the mixing time evaluation and the minimization functions definition for the late reverberation device. Taking into consideration these aspects, a deep analysis of hybrid digital reverberator audio quality is here proposed, introducing a new parameter for the definition of the mixing time and two new cost functions for the definition of the late reverberation parameters. More in detail, starting from the considerations derived from a previous accurate approach based on the mel frequency cepstral coefficients, the new cost functions are based on the evaluation of the perceptual linear predictive and power normalized cepstral coefficients. Several results are reported, in terms of objective measure, performance analysis and subjective measures, taking into consideration different real impulse responses and various input stimuli and making a comparison with the state of the art. In particular, the obtained results show that a good accuracy can be achieved also considering a low number of coefficients, therefore improving the computational performance.

This paper describes a system for recognizing distress calls and home automation voice commands in a smart-home. Distress calls are recognized with the purpose of assisting people in their own homes: when they are detected, a phone call is automatically established with a contact in a address book and the person can request for assistance. The voice call is established through a voice over ip stack, with hands-free communication guaranteed by an acoustic echo canceller. The acoustic environment is constantly monitored by several low-consuming devices distributed throughout the home. In each device, a voice activity detector detects speech segments, and a speech recognition engine recognizes commands and distress calls. Robustness to environmental disturbances has been increased by employing Power Normalized Cepstral Coefficients and by using an adaptive algorithm for interference cancellation. An Italian speech corpus of home automation commands and distress calls has been developed for evaluation purposes. The corpus has been recorded in a real room using multiple microphones, and each sentence has been uttered both in normal and shouted speaking styles. The system performance has been assessed in terms of commands/distress recognition accuracy in order to prove the effectiveness of the approach.

Multichannel acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes and to introduce better performance in terms of sound localization in the presence of more than one participant. Already stereophonic systems obtain realistic performance, since listeners have spatial information that helps to identify the speaker position. Unfortunately, the correlation between the two channels prevents a correct identification of the echo paths and signal decorrelation is needed. In this letter, a decorrelation approach is proposed, combining the missing-fundamental theory with frequency shifting. Therefore, the proposed approach is based on the computation of an adaptive parameter related to the fundamental frequency that is also used for controlling the desired frequency shift value. Experiments confirm that high performance in terms of magnitude squared coherence reduction and convergence speed increase can be obtained, also comparing it with other approaches of the state of the art and outperforming previous results.

The optimization of energy consumption, with consequent costs reduction, is one of the main challenges in present and future smart grids. Of course, this has to occur keeping the living comfort for the end-user unchanged. In this work, an approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed. The proposed algorithm yields an optimal task scheduling under dynamic electrical constraints, while simultaneously ensuring the thermal comfort according to the user needs. On purpose, a suitable thermal model based on heat-pump usage has been considered in the framework. Some computer simulations using real data have been performed, and obtained results confirm the efficiency and robustness of the algorithm, also in terms of achievable cost savings.

This paper deals with methods for multiple position room response equalization. Differently from a well-known technique working in the time domain and based on fuzzyc-means clustering, in the proposed approach most of the operations are performed in the frequency domain and, in particular, the fuzzyc-means clustering is applied to the room magnitude responses at different positions. It is shown that working in the frequency domain allows us to obtain equalization performances at least similar to those of the time domain approach with a strongly reduced computational complexity. In addition, different techniques that can replace the fuzzyc-means clustering algorithm in the derivation of the prototype room response equalizer, with additional reduction of the number of operations, are discussed. Finally, the results of three sets of experiments are used to illustrate the performance, the robustness and the quality of the proposed room response equalization method using alternative prototype design strategies applied to different environments.

In teleconferencing systems, undesired echoes due to coupling between the loudspeaker and the microphone can be reduced using acoustic echo cancellers (AECs). In order to introduce better performance in terms of sound localization, at least stereophonic AECs (SAECs) have to be employed. In this case, the correlation between the two channels prevents a correct identification of the echo paths and a signal decorrelation is needed. In this paper, a novel mixed approach to signal decorrelation is proposed based on the estimation of the fundamental frequency. The principle of the proposed structure is to use an approach based on the “missing-fundamental” phenomenon at low frequencies (below 500 Hz), a time-varying all-pass filter controlled by the fundamental frequency at medium and high frequencies, and a time-varying multi-notch filter for improving performance in the upper part of the spectrum. Experiments confirm the effectiveness of the proposed approach in terms of correct identification of the echo paths and stereo perception preservation.

Acoustic echo cancelers (AECs) are used in teleconferencing systems to reduce undesired echoes originating from coupling between the loudspeaker and the microphone. Better performance in terms of sound localization can be achieved only by using multichannel AECs. By the use of a two-channel (stereo) system, it is already possible to obtain more realistic performance than the monochannel case because listeners have more spatial information that helps to identify the speaker position. In this paper, a novel approach to stereo AEC is proposed based on a combination of psychoacoustic effects. The principle of the proposed structure is to use an approach based on the “missing-fundamental” phenomenon at low frequencies (below 500 Hz) and a modified phase modulation approach at higher frequencies. Several results are presented in terms of magnitude-squared coherence, Itakura-Saito measure, convergence speed of adaptive filters, interaural cross-correlation, and subjective evaluation, in order to confirm the validity of the proposed approach.

Decorrelation is a well known issue in the context of Stereophonic Acoustic Echo Cancellation: it is related to the problem of uniquely identifying each pair of room acoustic paths, due to high inter-channel coherence. In this paper, a novel approach to decorrelate a stereo signal based on the missing fundamental phenomenon is proposed. An adaptive algorithm is employed to track the behavior of one of the two channels, ensuring a continuous decorrelation without affecting the stereo quality. Several results are presented comparing our approach with Masked Noise injection method in terms of Magnitude Square Coherence, Itakura Saito measure and Convergence Speed of adaptive filters in order to confirm the validity of the proposed approach.

This paper proposes innovative multi-channel bayesian estimators in the feature-domain for robust speech recognition. Both minimum-mean-squared-error (MMSE) and maximum-a-posteriori (MAP) criteria have been explored: the related algorithms extend the multi-channel frequency-domain counterparts and generalize the single-channel feature-domain MMSE solution, recently appeared in the literature. Computer simulations conducted on a modified AURORA2 database show the efficacy of the frequency-domain multi-channel estimators when used as a pre-processing stage of a speech recognition engine, and that the proposed multi-channel MAP approach outperforms single-channel estimators by at least 3% on average.

The present work gives an interpretation of the discrete Stockwell transform (DST) from the theory of frame (TOF) perspective, showing first of all that the complete set of expansion functions of the DST is a frame. Starting from this interpretation, two versions of the DST are derived that allow to get a reduced redundancy representation of the original time series, when compared to the complete DST: the first method is based on a dyadic tiling of the time-frequency plane and leads to a perfect reconstruction representation using O(N) coefficients, whilst the second method is inspired by the matching pursuit technique and only provides an approximate representation of the original time series, but achieves better performances in terms of compactness, useful property in many practical applications.

The identification of cell cycle-related genes is still a difficult task, even for organisms with relatively few genes such as the fission yeast. Several gene expression studies have been published on S. pombe showing similarities but also discrepancies in their results. We introduce a network in which the weight of each link is a function of the phase difference between the expression peaks of two genes. The analysis of the stability of the clustering through the computation of an entropy parameter reveals a structure made of four clusters, the first one corresponding to a robustly connected M-G1 component, the second to genes in the S phase, and the third and fourth to two G2 components. They are separated by bottleneck structures that appear to correspond to cell cycle checkpoints. We identify a number of genes that are located on these bottlenecks. They represent a novel group of cell cycle regulatory genes. They all show interesting functions, and they are supposed to be involved in the regulation of the transition from one phase to the next. We therefore present a comparison of the available studies on the fission yeast cell cycle and a general statistical bioinformatics methodology to find bottlenecks and gene community structures based on recent developments in network theory.

This letter deals with the independent component analysis (ICA) problem in the complete case. As appeared recently in the literature, different Riemannian metrics can be defined within the parameter space (i.e., the general linear group), allowing to derive correspondingly various ICA learning rules based on the relative natural gradients (NGs). This letter proposes a general framework to analyze the stability of such learning rules, including the already published study focusing on the Amari's NG approach as a special case thereof. In particular, it is shown that the stability conditions known in the literature still hold in all cases addressed

In this paper some alternative Riemannian metrics are defined on the parameter space of non-square matrices, corresponding to various translations defined therein. Such metrics allow the authors to derive novel learning rules for two ICA based algorithms for over-determined blind source separation (BSS), which tries to separate less sources from more sensors. Computer simulations show a significant improvement of the convergence speed when second-order translations are employed in contrast to their first-order counterparts, extending known results for complete BSS

Several contributions in literature have recently proposed techniques based on assumption of source sparsity in some representation domain to give a solution to the problem of blind source separation in the underdetermined case. This work investigates how to employ wavelet based sparse representation of signals in an already existing algorithm for the problem under study, in order to improve separability of sources, in comparison to application of short time Fourier transform. Different wavelet transforms are considered. Moreover, this approach allows to perform a suitable de-noising operation after the separation algorithm, by thresholding the wavelet coefficients corresponding to extracted sources. This occurs at a very low computational cost, resulting in a further improvement of source recovering when noise is present at mixture level. Experimental results confirm the effectiveness of what implemented.

In the past few years, researchers have been extensively studying the application of recurrent neural networks (RNNs) to solving tasks where detection of long term dependencies is required. This paper proposes an original architecture termed the Recurrent Multiscale Network, RMN, to deal with these kinds of problems. Its most relevant properties are concerned with maintaining conventional RNNs' capability of information storing whilst simultaneously attempting to reduce their typical drawback occurring when they are trained by gradient descent algorithms, namely the vanishing gradient effect. This is achieved through RMN which preprocesses the original signal separating information at different temporal scales through an adequate DSP tool, and handling each information level with an autonomous recurrent architecture; the final goal is achieved by a nonlinear reconstruction section. This network has shown a markedly improved generalization performance over conventional RNNs, in its application to time series prediction tasks where long range dependencies are involved.

In this paper we propose a new approach for music features extraction used for fast content-based retrieval of songs from a suitable built database. The algorithm discovers, by an interaction with the program manager, the refrain of type O MIDI songs and builds a database in which musical features and text information such as title, author, genre, etc. are stored. A neural network architecture is trained only by the features from the refrain of all the songs in the database belonging to an appropriate sub-class, and performs the retrieval in query-by-humming problem kind. Elman recurrent neural nets are used. Experimental results show the effectiveness of the proposed approach.

In this paper we present the results of applying adaptive nonlinearities and maximum entropy principle to identify an inverting filter for the post nonlinear blind source deconvolution problem. The filter is a cascade of a linear FIR matrix and a nonlinear memoryless componentwise system. Cubic splines and polynomials have been selected as adaptive parametric functions. A fast frequency implementation is also proposed.

This paper proposes a possible solution to the vanishing gradient problem in recurrent neural networks, occurring when such networks are applied to solving tasks where detection of long term dependencies is required. The main idea consists of pre-processing the signal (a time series typically) through a discrete wavelet decomposition, in order to separate the short term information from the long term ones, and treating each scale by different recurrent neural networks. The partial results concerning all the sequences at diverse time/frequency resolutions are combined through an adaptive nonlinear structure in order to achieve the final goal. This new preprocessing based approach is distinct from the other one reported in literature to-date, as it tends to mitigate the effects of the problem under study avoiding relevant changing in network's architecture and learning techniques. The overall system (called recurrent multiscale network, RMN) is described and its performances tested through typical tasks namely the latching problem and time series prediction.

In this paper, neural networks based on an adaptive nonlinear function suitable for both blind complex time domain signal separation and blind frequency domain signal deconvolution, are presented. This activation function, whose shape is modified during learning, is based on a couple of spline functions, one for the real and one for the imaginary part of the input. The shape control points are adaptively changed using gradient-based techniques. B-splines are used, because they allow to impose only simple constraints on the control parameters in order to ensure a monotonously increasing characteristic. This new adaptive function is then applied to the outputs of a one-layer neural network in order to separate complex signals from mixtures by maximizing the entropy of the function outputs. We derive a simple form of the adaptation algorithm and present some experimental results that demonstrate the effectiveness of the proposed method.

This paper focuses on a newly developed transparent nADPCMBJ⊥MLT speech coding algorithm. Our coder first decomposes the narrowband speech signal in subbands, a non linear ADPCM scheme is then performed in each subband. The signal subband decomposition is piloted by the equivalent Modulated Lapped Transform (MLT) filter bank. The novelty of this algorithm is the non linear approach, based on neural networks, to subband prediction coding. We have evaluated the performance of the nADPCMB⊥MLT coding algorithm with a session of formal listening based on the five grade impairment scale standardized within ITU - T Recommendation P.800.

In this paper a new neural network model for blind demixing of nonlinear mixtures is proposed. We address the use of the adaptive spline neural network recently introduced for supervised and unsupervised neural networks. These networks are built using neurons with flexible B-spline activation functions and in order to separate signals from mixtures, a gradient-ascending algorithm which maximizes the outputs entropy is derived. In particular a suitable architecture composed by two layers of flexible nonlinear functions for the separation of nonlinear mixtures is proposed. Some experimental results that demonstrate the effectiveness of the proposed neural architecture are presented.

This paper presents a new neural architecture suitable for digital signal processing application. The architecture, based on adaptable multidimensional activation functions, allows one to collect information from the previous network layer in aggregate form. In other words the number of network connections (structural complexity) can be very low respect to the problem complexity. This fact, as experimentally demonstrated in the paper, improve the network generalization capabilities and speed up the convergence of the learning process. A specific learning algorithm is derived and experimental results, on channel equalization, demonstrate the effectiveness of the proposed architecture.

An adaptive nonlinear function for blind signal separation is presented. It is based on a spline approximation whose control points are adaptively changed using information maximization techniques. The monotonously increasing characteristic is obtained using suitable B-spline functions imposing simple constraints on its control points. In particular, the problem of adaptively maximizing the entropy of the output is considered in the context of blind separation of independent sources. We derive a simple form of the learning algorithm which allows us not only to adapt the separation matrix coefficients but also the shape of the nonlinear functions. A comparison with the mixture-of-densities approach is also presented on some experimental data that demonstrates the effectiveness and efficiency of the proposed method.

A large class of nonlinear dynamic adaptive systems, such as dynamic recurrent neural networks, can be very effectively represented by signal-flow-graphs. Using this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input one-output transformation, as in an electrical circuit. Following an approach originally developed by Lee (1974) for continuous-time systems based on the concept of adjoint graph, a new algorithm to estimate the derivative of the output with respect to an internal parameter have been proposed in the literature for discrete-time systems. This paper extends further this approach to multirate digital systems, which have been widely used. The new method can be employed for gradient-based learning of general multirate circuits, such as the new "multirate" neural networks.

This paper proposes a virtual stereo system that behaves like a stereo system but requires standard mono-aural communication channel. Since many human conversations are single talker, at the far-end side the system obtains a single channel signal by performing a Direction-Of-Arrival (DOA) estimation and a passive beamforming on the true stereo signal, while at the near-end side it uses an active beamformer to reproduce the stereo effect. A single acoustic echo canceler can now be used, with some advantages over the true stereophonic system. The information on the direction of arrival can be included into the coded digital stream or can be perceptually hidden into the speech signal over standard analog channels.

This paper aims at recalling recent results about neural Minor Component Analysis and to apply them to spatial adaptive array filtering (adaptive beamforming). The constrained beamformer power optimization principle is employed, which allows us to improve the performances of simpler beamforming algorithms by emphasizing white noise sensitivity control and prior knowledge about the disturbances.

A large class of nonlinear dynamic adaptive systems such as dynamic recurrent neural networks can be effectively represented by signal flow graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input, one-output transformation, as in an electrical circuit. Even if graph representations are popular in the neural network community, they are often used for qualitative description rather than for rigorous representation and computational purposes. In this article, a method for both on-line and batch-backward gradient computation of a system output or cost function with respect to system parameters is derived by the SFG representation theory and its known properties. The system can be any causal, in general nonlinear and time-variant, dynamic system represented by an SFG, in particular any feedforward, time-delay, or recurrent neural network. In this work, we use discrete-time notation, but the same theory holds for the continuous-time case. The gradient is obtained in a straightforward way by the analysis of two SFGs, the original one and its adjoint (obtained from the first by simple transformations), without the complex chain rule expansions of derivatives usually employed. This method can be used for sensitivity analysis and for learning both off-line and on-line. On-line learning is particularly important since it is required by many real applications, such as digital signal processing, system identification and control, channel equalization, and predistortion.

This paper presents a new cost function for blind deconvolution of non-minimum phase systems. The proposed criterion arises as a natural consequence of a fundamental theorem proved by Benveniste, Goursat and Ruget (1980), and appears to be the weighted square of the difference among two spectra, thus its minimization leads to a weighted least-squares blind deconvolution technique. In order to assess the new theory some simulation results both on ideal (noiseless) and noisy channels are presented.

This paper presents a non-linear extension of the Sanger's (1989) generalized Hebbian algorithm to the processing of complex-valued data. A possible choice of the involved nonlinearity is discussed recalling the Sudjianto-Hassoun (1994) interpretation of the nonlinear Hebbian learning. Extension of this interpretation to the complex case leads to a nonlinearity called the Rayleigh function, which allows for separating mixed independent complex-valued source signals.

A new system for speech quality enhancement (SQE) is presented. A SQE system attempts to recover the high and low frequencies from a narrow-band speech signal, usually working as a post-processor at the receiver side of a transmission system. The new system operates directly in the frequency domain using complex-valued neural networks. In order to reduce the computational burden and improve the generalization capabilities, a new architecture based on a previously introduced neural network, called the adaptive spline neural network (ASNN), is employed. Experimental results demonstrate the effectiveness of the proposed method.

This paper shortly describes a preliminary version of a new discrete-time network software simulator implemented using the signal-flow-graph (SFG) approach. The software is able to do a very user-friendly schematic entry of the network and the cost function, and can implement any discrete-time network composed by delays, linear and nonlinear fired or adaptable components (also user-defined). Structures such as usual linear filters (FIR, IIR) and static multilayer, time delay or recurrent neural networks of arbitrary topologies can be easily adapted with respect to any user-selected parameters, minimizing any objective function that can be described by a SFG (online, batch, windowed MSE, etc.) using the BC method.

This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal recursive backpropagation (CRBP), presents some advantages with respect to the other on-line training methods. The new CRBP algorithm includes as particular cases backpropagation (BP), temporal backpropagation (TBP), backpropagation for sequences (BPS), Back-Tsoi algorithm among others, thereby providing a unifying view on gradient calculation techniques for recurrent networks with local feedback. The only learning method that has been proposed for locally recurrent networks with no architectural restriction is the one by Back and Tsoi. The proposed algorithm has better stability and higher speed of convergence with respect to the Back-Tsoi algorithm, which is supported by the theoretical development and confirmed by simulations. The computational complexity of the CRBP is comparable with that of the Back-Tsoi algorithm, e.g., less that a factor of 1.5 for usual architectures and parameter settings. The superior performance of the new algorithm, however, easily justifies this small increase in computational burden. In addition, the general paradigms of truncated BPTT and RTRL are applied to networks with local feedback and compared with the new CRBP method. The simulations show that CRBP exhibits similar performances and the detailed analysis of complexity reveals that CRBP is much simpler and easier to implement, e.g., CRBP is local in space and in time while RTRL is not local in space.

In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the ASNN's high representation capabilities, networks with a small number of interconnections can be trained to solve both pattern recognition and data processing real-time problems. The main idea is to use a Catmull-Rom cubic spline as the neuron's activation function, which ensures a simple structure suitable for both software and hardware implementation. Experimental results demonstrate improvements in terms of generalization capability and of learning speed in both pattern recognition and data processing tasks.

This paper presents a new technique to control stability of IIR adaptive filters based on the idea of intrinsically stable operations that makes it possible to continually adapt the coefficients with no need of a stability test or pole projection. The coefficients are adapted in a way that intrinsically assures the poles to be in the unit circle. This makes it possible to use a higher step size (also named learning rate here) potentially improving the fastness of adaptation with respect to methods that employ a bound on the learning rate or methods that simply do not control stability. This method can be applied to various realizations: direct forms, cascade or parallel of second order sections, lattice forms. It can be implemented to adapt a simple IIR adaptive filter or a locally recurrent neural network such as the IIR-MLP.

We propose a new learning algorithm for locally recurrent neural networks, called truncated recursive backpropagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. (1989) for TDNN, and includes the Back and Tsoi (1991) algorithm as well as BPS and standard on-line backpropagation as particular cases. The proposed algorithm has a memory and computational complexity that can be adjusted by a careful choice of two parameters h and h' and so it is more flexible than a previous algorithm proposed by us. Although for the sake of brevity we present the new algorithm only for IIR-MLP networks, it can be applied also to any locally recurrent neural network. Some computer simulations of dynamical system identification tests, reported in literature, are also presented to assess the performance of the proposed algorithm applied to the IIR-MLP.

We derive a new class of neural unsupervised learning rules which arises from the analysis of the dynamics of an abstract mechanical system. The corresponding algorithms can be used to solve several problems in the area of digital signal processing, where orthonormal matrices are involved. We present an application which deals with blind separation of sources, i.e. a new method to perform efficient independent component analysis (ICA) of random signals.

Neural networks with internal temporal dynamics can be applied to non-linear DSP problems. The classical fully connected recurrent architectures, can be replaced by less complex neural networks, based on the well known multilayer perceptron (MLP) where the temporal dynamics is modelled by replacing each synapses either with an FIR filter or with an IIR filter. A general learning algorithm (back-propagation-through-time or BPTT) for a dynamic neural MLP has been introduced by P.J. Werbos (1990). This is a non-causal algorithm, being able to work only in batch mode, while many real problems require on-line adaptation. In this paper we show a new on-line learning algorithm for the IIR-MLP networks which is an approximation of the true BPTT and which includes, as a particular case, the already known Back and Tsoi (1991) algorithm. Several computer simulations of identification of dynamical systems are also reported.

For high-speed low-complexity filter design, it is common practice to constrain the filters' coefficients to be a power-of-two or a sum of powers-of-two terms (P2), avoiding the full multiplication. Tapped interconnection of different sub-filters are sometimes used to enhance the ripple and stop-band attenuation performances. An extension of the simple cascade architectures, suitable for hardware implementation, is the polynomial sharpening technique. Firstly proposed by Kaiser-Hamming, it allows the use of identical sub-filters with a small hardware overhead. We propose a new approach to the design of P2 sharpening filters based on a specific genetic algorithm which optimizes both the FIR sub-filter and the sharpening polynomial coefficients expressed as P2 terms. This allows one to obtain better performances than the classical P2 design techniques when FIR filters with long impulse responses are involved.

This paper presents an efficient genetic approach to the design of digital finite impulse response (FIR) filters with coefficients constrained to be sums of power-of-two terms. To obtain such efficiency, i.e. a reduction of computational costs and an improvement in performance, a specific filter coefficient coding scheme has been studied and implemented. The resulting genetic algorithm (GA) is explained and compared experimentally with other state-of-the-art design techniques on several power-of-two FIR filter design cases. It can be seen that the proposed genetic technique is able to attain results as good as or better than the other methods. Moreover it can be easily implemented on parallel hardware.

It is known that RNS VLSI processors can parallelize fixed-point addition and multiplication operations by the use of the Chinese remainder theorem (CRT). The required modular operations, however, must use specialized hardware whose design and implementation can create several problems. In this paper a modified residue arithmetic, called pseudo-RNS is introduced in order to alleviate some of the RNS problems when digital signal processing (DSP) structures are implemented. Pseudo-RNS requires only the use of modified binary processors and exhibits a speed performance comparable with other RNS traditional approaches. Some applications of the pseudo-RNS to common DSP architectures, such as multipliers and filters, are also presented in this paper. They are compared in terms of the area-time square product versus other RNS and weighted binary structures. It is proven that existing combinatorial or look-up table approaches for RNS are tailored to small designs or special applications, while the pseudo-RNS approach remains competitive also for complex systems.>

In this paper an alternative algorithm for the Singular Value Decomposition (SVD) of the data matrix used for Direction-Of-Arrival (DOA) estimation is presented. The proposed algorithm transforms the data matrix into a bi-diagonal form by a procedure based on fast Givens rotations. This procedure is specifically tailored for DOA estimation and allows a certain computational saving with respect to the well known Golub-Kahan algorithm.>

In highly integrated processors, a concurrent fault tolerance capability is particularly important, especially for real-time applications. In fact, in these systems, transient errors are often present, but are difficult to correct online. Error recovery procedures applied for each processing or memory element require large amount of hardware and can reduce throughput. Residue arithmetic has intrinsic fault tolerance capability and requires less complex hardware. A single error correction procedure based on the use of a redundant residue number system (RRNS) and the base extension operation is proposed. The method uses a very small decision table and works in parallel mode; therefore it is suitable for high speed VLSI circuit realization. A parallel systolic architecture which realizes the algorithm is introduced.>

Multilayer perceptrons (MLPs) with weight values restricted to powers of two or sums of powers of two are introduced. In a digital implementation, these neural networks do not need multipliers but only shift registers when computing in forward mode, thus saving chip area and computation time. A learning procedure, based on backpropagation, is presented for such neural networks. This learning procedure requires full real arithmetic and therefore must be performed offline. Some test cases are presented, concerning MLPs with hidden layers of different sizes, on pattern recognition problems. Such tests demonstrate the validity and the generalization capability of the method and give some insight into the behavior of the learning algorithm.

After an ad hoc extension of the multilayer perceptron model to complex signals, a blind neural network equalizer is proposed. Several experimental results are also reported which prove the effectiveness of this approach for equalizing a digital satellite radio channel in the presence of mild nonlinearities and intersymbol interference. Moreover, the proposed method works without any knowledge about the nonlinear nature of the channel.>

A procedure for assigning optimal weights to the prediction equations which are used to obtain the parameters of an autoregressive (AR) model for spectrum estimation by the least squares (LS) solution is presented. The set of weights is computed, by linear programming techniques, in order to reduce the effects of strong impulsive noise onto the AR parameter estimate. The method is particularly effective when the Gaussian white noise component is much smaller than both spikes and useful signal. In order to demonstrate the capability of the proposed approach, the results of a simple AR parameter estimation experiment are also reported.>

Fast RNS (residue number system) algorithms which use only binary arithmetic are developed. Scaled residues, called pseudoresidues, are introduced by exploiting the cycle properties of each RNS channel and solving a Diophantine equation. Using the pseudoresidues instead of the original residue set to perform the desired computations, an RNS processor can be built with standard binary devices of small wordlength. The effectiveness of the procedure is shown by developing the pseudoresidue implementations of a modular multiplier for odd moduli RNS and of a FIR (finite impulse response) filter. The resulting structures exhibit complete reprogrammability for both moduli and coefficients, a very low number of fast machine cycles, and a square time-area product reduction.>

The problem of registration of displaced images is considered. A well known method for this problem is the phase-correlation algorithm, which provides superior performance when rigid plane translation may be expected, even if the images are noisy and subject to varying conditions of illumination. However, the computational cost of this approach is high since it requires complex arithmetics. The computational cost can be reduced by using the correlation function between the binary boundary maps of the images, computed by Fermat number transforms. Based on this approach, an enhanced method which uses the Burg filter to whiten the image sequences before the contour extraction is proposed. The experimental results prove the superior performance of the proposed method with respect to direct correlation.>

The feasibility of restricting the weight values in multilayer perceptrons to powers of two or sums of powers of two is studied. Multipliers could be thus replaced by shifters and adders on digital hardware, saving both time and chip area, under the assumption that the neuron activation function is computed through a lookup table (LUT) and that a LUT can be shared among many neurons. A learning procedure based on back-propagation for obtaining a neural network with such discrete weights is presented. This learning procedure requires full real arithmetic and therefore must be performed offline. It starts from a multilayer perceptron with continuous weights learned using back-propagation. Then a weight normalization is made to ensure that the whole shifting dynamics is used and to maximize the match between continuous and discrete weights of neurons sharing the same LUT. Finally, a discrete version of BP algorithm with automatic learning rate control is applied up to convergence. Some test runs on a simple pattern recognition problem show the feasibility of the approach

The possibility of using the multilayer perceptron (MLP) neural network for the processing of the evoked potentials (EPs) is analyzed. In this case, the process can be conceived as deterministic low amplitude signals (damped sine waves), corresponding to the brain's response to stimuli, embedded in strongly colored noise, the EEG background activity. Typical values of the signal-to-noise ratio are less than 0 dB. The network, used as a nonlinear filter, is trained using iteratively as the input signal one of a set of available EP ensembles and as the target signal another ensemble of the same set. Experimental results, both on synthetic and real data, show that the method provides good results with very few EP ensembles. Therefore, it allows a noteworthy reduction of the signal nonstationarity and the patient's annoyance

A very versatile algorithm for the design of finite-wordlength filters is described. It is a simulated annealing algorithm, derived directly from the simulated annealing algorithm for functions of continuous variables which searches over discrete values of the filter coefficients. It has very fast development time and produces filters with good performances. Its only drawback is a very long execution time. Three examples of filter design are included.>

An important problem in real-time DSP (digital signal-processing) systems with highly integrated components is the capability of automatic error detection and correction. The use of residue number arithmetic allows error detection and correction because of its unweighted nature. A single-error-correction procedure is proposed which is based on the use of redundant residue number systems (RRNS) and the base extension operation. The proposed method uses a small decision table and works in parallel mode; therefore it is suitable for high-speed VLSI circuit realization. A parallel architecture which realizes the method is also introduced.>