This work presents a methodology to generate a mobility-pattern database for urban traffic monitoring through distributed acoustic sensing. Registers from the continuous monitoring of a live experimental testbed provide three canonical types of events (buses, cars and pedestrians). Their separability for automatic detection and classification is inspected through PCA analysis of their approximate entropy and Hjorth parameters. An automatic detection and labeling procedure is presented and used to generate a database of labeled files accessible and usable for future machine learning approaches.

Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR’16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.

The automatic classification of volcano-seismic events is a key problem in volcanology. Due to its complexity, deep learning (DL) techniques have become the tool of choice for this problem, outperforming classical classifiers. The main drawback of this approach, when applied to the classification of volcano-seismic events, is its tendency to overfit because of the small-size available databases. In this work, we propose and analyze the use of the Gaussian processes (GPs) and Deep GPs (DGPs), and their hierarchical extension, for volcano-seismic event classification. We empirically prove the adequacy of the proposed modeling with an insightful and exhaustive comparison with state-of-the-art DL-based methods on a seismic database recorded at “Volcán de Fuego,” Colima, Mexico. The hierarchical structure of DGPs and the reduced number of parameters to be automatically estimated become essential to achieve excellent performance even on small databases, capturing well the complex patterns of seismic signals for all classes and, in particular, for those that have been hardly observed.

We present an S-phase picking algorithm for volcano-tectonic earthquakes (VTs), based on the changes of frequency and amplitude expected in the plane transverse to the ray direction at S-phase arrival. A measure of these changes, called spectral dissimilarity, is proposed. Picking is performed in a particular waveform transformation that underlines such variations foreseen: horizontal instant power. Then, the algorithm provides a measure of its reliability, grounded on the low or high fluctuations of the picking instant obtained when applied to other horizontal components of the seismogram. Experiments are performed to test the algorithm with a challenging database of volcano seismic earthquakes from Mt. Etna, carefully picked and labeled by a human expert. The technique is compared to two well-known S-phase pickers: one based on the damped predominant period analysis and the other based on polarization and kurtosis rate analysis. The algorithm improves these techniques for the particular scenario of VTs, providing interesting results and possibilities of the application.

Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at “Volcán de Fuego”, Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.

This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995-2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016-2017 by the Spanish Antarctic scientific campaign. Despite the variations in the geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, even if seismic sources change over time.

This letter presents a novel picking algorithm which allows an automated determination of the P-phase onset time. The algorithm includes an adaptive multiband processing and noise-reduction techniques to allow a confident onset time estimation in signals strongly affected by background and/or nonstationary noise processes. Results using a set of 3780 computer-generated earthquake-like signals show that the accuracy is much better than that achieved by conventional STA/LTA algorithm. In addition, the accuracy of the proposed method is improved when it is combined with an autoregressive method. An application of the algorithm to a set of 400 natural earthquakes confirms that the combination of both algorithms provides a precise P-phase onset time estimation in real environments, overcoming the limitations associated with the autoregressive method.

In this paper, we address the problem of robustness to both noise and speaker-variability in automatic speech recognition (ASR). We propose the use of pre-computed Noise and Speaker transforms, and an optimal combination of these two transforms are chosen during test using maximum-likelihood (ML) criterion. These pre-computed transforms are obtained during training by using data obtained from different noise conditions that are usually encountered for that particular ASR task. The environment transforms are obtained during training using constrained-MLLR (CMLLR) framework, while for speaker-transforms we use the analytically determined linear-VTLN matrices. Even though the exact noise environment may not be encountered during test, the ML-based choice of the closest Environment transform provides “sufficient” cleaning and this is corroborated by experimental results with performance comparable to histogram equalization or Vector Taylor Series approaches on Aurora-2 task. The proposed method is simple since it involves only the choice of pre-computed environment and speaker transforms and therefore, can be applied with very little test data unlike many other speaker and noise-compensation methods.

In this paper, we propose a method to compensate for noise and speaker-variability directly in the Log filter-bank (FB) domain, so that MFCC features are robust to noise and speaker-variations. For noise-compensation, we use Vector Taylor Series (VTS) approach in the Log FB domain, and speaker-normalization is also done in the Log FB domain using Linear Vocal tract length (VTLN) matrices. For VTLN, optimal selection of warp-factor is done in Log FB domain using canonical GMM model, avoiding the two-pass approach needed by a HMM model. Further, this can be efficiently implemented using sufficient statistics obtained from the GMM and the FB-VTLN-matrices. The warp-factor selection using GMM can also be done in cepstral domain by applying DCT matrices without the usual approximations associated with conventional linear-VTLN. The elegance of the proposed approach is that given the speech data, we obtain directly MFCC features that are robust to noise and speaker-variations. The proposed approach, show a significant relative improvement of 31% over baseline on Aurora-4 task.

Feature extraction is a critical element in automatic pattern classification. In this letter, we propose different sets of parameters for classification of volcano-seismic signals, and the discriminative feature selection (DFS) method is applied for selecting the minimum number of features containing most of the discriminative information. We have applied DFS to a conventional cepstral-based parameterization (with 39 features) and to an extended set of parameters (including 84 features). Classification experiments using seismograms recorded at Colima Volcano (Mexico) show that, for the most complex classifier and using the cepstral-based parameterization, DFS provided a reduction of the error rate from 24.3% (using 39 features) to 15.5% (ten components). When DFS is applied to the extended parameterization, the error rate decreased from 27.9% (84 features) to 13.8% (14 features). These results show the utility of DFS for identifying the best components from the original feature vector and for exploring new parameterizations for the classification of volcano-seismic signals.

This letter assesses an improved equalization transformation for robust speech recognition in noisy environments. The proposal is an evolution of the parametric approximation to Histogram Equalization named PEQ into a two-step algorithm dealing separately with environmental and acoustic mismatch. A first parametric equalization is done to eliminate environmental mismatch. These equalized data are divided into classes, and parametrically re-equalized using class specific references to reduce the acoustic mismatch. Experiments have been conducted for Aurora 2 and Aurora 4 databases. A comparative analysis of the experimental results shows significant benefits for databases with high acoustic variability like Aurora 4.

This paper describes the conceptual and algorithmic evolutions of Memory Based Parametric Equalization (MPEQ) needed to exploit the potentialities of the method within the state-of-the-art Loquendo ASR. MPEQ is the memory-based evolution of Parametric Non-Linear Equalization (PEQ) introduced to overcome the problem of unreliable statistics estimation in presence of very limited acoustic information in the test utterance to be normalized. The main limitations of the method that prevented its practical application were the lack of online implementation, the unrealistic unimodal assumption about the training statistics, the unconditioned application of equalization, and the need for retraining the acoustic models. The paper describes how these limitations have been overcome and reports a large experimentation on many corpora that shows improvements in a variety of mismatched conditions, while preserving performances in matched conditions.

This work deals with strategies to jointly reduce the speaker and environment mismatches in Automatic Speech Recognition. The consequences of environmental mismatch in the performance of conventional Vocal Tract Length Normalization algorithm are analyzed, observing the sensitivity of the warping factor distributions to the SNR fall. A new combined speaker-noise normalization strategy which reduces the effect of noise in VTLN by applying Histogram Equalization is proposed and experimented in AURORA2 and AU RORA4 databases. Solid results are obtained and discussed to analyze the effectiveness of the described technique.

This paper describes a novel modification of Histogram Equalization approach to robust speech recognition. We propose separate equalization of the high frequency and low frequency bands. We study different combinations of the sub-band equalization and obtain best results when we performs a twostage equalization. First, conventional Histogram Equalization (HEQ) is performed on the cepstral features, which does not completely equalize high frequency and low frequency bands, even though the overall histogram equalization is good. In the second stage, an equalization is done separately on the high frequency and the low frequency components of the above equalized cepstra. We refer to this approach as Sub-band Histogram Equalization (S-HEQ). The new set of features has better equalization of the sub-bands as well as the overall cepstral histogram. Recognition results show a relative improvement of 12% and 15% over conventional HEQ on Aurora-2 and Aurora4 databases respectively.

Monitoring of precursory seismicity in volcanoes is the most reliable and widely used technique in volcano monitoring. Since a visual inspection by human operators is a tedious task in a non-stop monitoring process, Hidden Markov Models have been previously proposed to automatically classify the different types of volcano-seismic events. Mel Frequency Cepstral Coefficients were successfully used as feature vector in this continuous classification system. In this paper seven novel features to be included in the MFCC feature vector are proposed. A very elementary GMM-based classifier has been implemented in order to assess the efficiency of the proposed parameters. Results using hundreds of events recorded from stations situated at Colima (Mexico) and Arenal (Costa Rica) volcanoes show that the proposed features improve the recognition accuracy and therefore they may be relevant in continuous volcano-seismic event automatic classification.

This work presents a continuous volcano-seismic classification system based in the Hidden Markov Models as solution to recently strong needs for automatic event detection and recognition methods in early warning and monitoring scenarios. Furthermore, our system includes a reliable method to assign confidence measures to the recognized signals in order to evaluate the robustness of the results. Data from the two most active volcanoes have been used to probe the system reliability on a complex joint corpus achieving a recognition accuracy higher than 78% in blind recognition tests.

This paper analyzes the benefits and drawbacks of PEQ, a features normalization technique based on the parametric equalization of the MFCC parameters to match a reference probability distribution. Three evolutions of PEQ are presented as solutions to the limitations encountered. The effects of those proposed evolutions are studied on three speech corpora, namely WSJ0, AURORA-3 and HIWIRE cockpit database. Simple and more complex evaluation tasks with different mismatch conditions given by convolutional and/or additive noise and non-native speakers, are evaluated to analyze the improvements.

In this paper, a unified approach to speech enhancement, feature extraction and feature normalization for speech recognition in adverse recording conditions is presented. The proposed frontend system consists of several different, independent, processing modules. Each of the algorithms contained in these modules has been independently applied to the problem of speech recognition in noise, significantly improving the recognition rates. In this work, these algorithms are merged in a single front-end and their combined performance is demonstrated. Specifically, the proposed advanced front-end extracts noise-invariant features via the following modules: Wiener filtering, voice-activity detection, robust feature extraction (nonlinear modulation or fractal features), parameter equalization and frame-dropping. The advanced front-end is applied to extremely adverse environments where most feature extraction schemes fail. We show that by combining speech enhancement, robust feature extraction and feature normalization up to a fivefold error rate reduction can be achieved for certain tasks.

In this paper, we propose a noise compensation method for robust speech recognition in DSR (Distributed Speech Recognition) systems based on histogram equalization and correlation information. The objective of this method is to exploit the correlation between components of the feature vector and the temporal correlation between consecutive frames of each component. The recognition experiments, including results in the Aurora 2, Aurora 3-Spanish and Aurora 3-Italian databases, demonstrate that the use of this correlation information increases the recognition accuracy. Index Terms: Distributed Speech Recognition, noise compensation, histogram equalization, correlation information

This paper shows an improved statistical test for voice activity detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The motivations for revising the original multiple observation LRT (MO-LRT) are found in its artificially added hangover mechanism that exhibits an incorrect behavior under different signal-to-noise ratio (SNR) conditions. The new approach defines a maximum a posteriori (MAP) statistical test in which all the global hypotheses on the multiple observation window containing up to one speech-to-nonspeech or nonspeech-to-speech transitions are considered. Thus, the implicit hangover mechanism artificially added by the original method was not found in the revised method so its design can be further improved. With these and other innovations, the proposed method showed a higher speech/nonspeech discrimination accuracy over a wide range of SNR conditions when compared to the original MO-LRT voice activity detector (VAD). Experiments conducted on the AURORA databases and tasks showed that the revised method yields significant improvements in speech recognition performance over standardized VADs such as ITU T G.729 and ETSI AMR for discontinuous voice transmission and the ETSI AFE for distributed speech recognition (DSR), as well as over recently reported methods.

This paper presents VIPACES (Verification Interface Primitives for the development of AXI Compliant Elements and Systems), a simple environment for the verification of AMBA 3 AXI systems in Verification IP (VIP) production. The elements come from the necessity of creating generic modules, in the verification phase, for this widely used protocol. These primitives are presented as a not compiled library written in SystemC where interfaces are the core of the library. The definition of interfaces instead of generic modules let the user construct custom modules improving the resources spent during the verification phase as well as easily adapting his own modules to the AMBA 3 AXI protocol. As validation scenario, results obtained for an AXI bus connecting IDCT and other processing resources for MPEG4 video decoding are presented.

A new front-end normalization algorithm that uses a parametric nonlinear transformation is proposed in this paper. The method improves histogram equalization based nonlinear transformations by finding a simple and computationally inexpensive parametric expression of the nonlinear transformation. The new parametric approach relies on a two Gaussian model for the probability distribution of the features, and on a simple Gaussian classifier to label the input frames as belonging to the speech or non-speech classes. The result is a more robust equalization, less dependent on the percentage of speech and non-speech frames. Recognition experiments on the AURORA 4 database have been performed and the effectiveness of the algorithm is analyzed in comparison with other linear and nonlinear feature equalization techniques

This paper shows an effective voice activity detector based on a statistical likelihood ratio test defined on the integrated bispectrum of the signal. It inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: i) its computation as a cross spectrum leads to significant computational savings, and ii) the variance of the estimator is of the same order as that of the power spectrum estimator. The proposed method incorporates contextual information to the decision rule, a strategy that has reported significant improvements in speech detection accuracy and robust speech recognition applications. The experimental analysis conducted on the well-known AURORA databases has reported significant improvements over standardized techniques such as ITU G.729, AMRI, AMR2 and ESTI AFE VADs, as well as over recently published VADs

This paper presents a preliminary study of an optical flow-based parameterization of visual information in a sign language recognition system using Hidden Markov Models (HMM). Current feature extraction processes need initialization, tracking and segmentation stages in order to describe signer gestures. Our aim is to develop a single and fast technique to reduce computational complexity which doesn't require these stages and is able to work in mobile devices with limited hardware resources. The Moving Block Distance (MBD) parameterization is an interesting first approach for this purpose, proved by two signers under a static background constraint. A lexicon of 33 basic word units (signemes) was used to build the data set containing phrases with a variable number of words. Continuous recognition results achieve more than 99 % accuracy in close test. Index Terms: multi-modal recognition, visual feature extraction, mobile devices, sign language, optical flow. 1.

A filter that introduces inter-frame information into the voice features set is proposed in this paper. The filter adds the autocorrelations of the cepstral coefficients to the set of characteristics used for training and recognition. Those autocorrelations should not depend on the environment conditions. Because they should only depend on the information to recognize, a normalization of that inter-frame information is convenient. The filter defined implements this normalization by transforming the autocorrelations into a normalized domain defined with clean adaptation data. This temporal processing of the features is added to the Histogram Equalization of the cepstral coefficients (HEQ) used to normalize the MFCCs. An analysis is done about the most effective domain (original MFCCS or equalized MFCCs) on which the temporal processing should be executed. Performance results for the proposed algorithm are presented for AURORA2 and AURORA4 databases.

This paper shows an effective speech/non-speech discrimination method for improving the performance of speech processing systems working in noisy environment. The proposed method uses a trained support vector machine (SVM) that defines an optimized non-linear decision rule over different sets of speech features. Two alternative feature extraction processes based on: i) subband SNR estimation after denoising, and ii) long-term SNR estimation were compared. Both methods show the ability of the SVM-based classifier to learn how the signal is masked by the acoustic noise and to define an effective non-linear decision rule. However, it is shown that a feature vector incorporating contextual information yielded better speech/non-speech discrimination even when no denoising is applied. The experimental analysis carried out on the Spanish SpeechDat-Car database shows clear improvements over standard VADs including ITU G.729, ETSI AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs. Index Terms: voice activity detection, support vector machine learning, speech enhancement.

We propose a model-based VAD derived from the Vector Taylor Series (VTS) approach. A Gaussian mixture (trained with clean speech) is used in order to provide an appropriate decision rule for speech/non-speech detection. Additionally, VTS approach adapts the Gaussian mixture to noise conditions, yielding a stable perfor- mance for a wide range of SNRs. We have evaluated its ability for speech/non-speech detection and also its application for robust speech recognition. When compared to other VAD methods, the proposed VAD shows the best trade-off in speech/non-speech de- tection. When applied for Wiener Filtering and for frame drop- ping, the proposed VAD also provides the best recognition results. Index Terms: voice activity detection (VAD), vector Taylor series approach (VTS), Gaussian mixture, Wiener filtering.

Currently, there are technology barriers inhibiting speech processing systems that work in extremely noisy conditions from meeting the demands of modern applications. This letter presents a new voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm defines an optimum likelihood ratio test (LRT) involving multiple and independent observations. The so-defined decision rule reports significant improvements in speech/nonspeech discrimination accuracy over existing VAD methods that are defined on a single observation and need empirically tuned hangover mechanisms. The algorithm has an inherent delay that, for several applications, including robust speech recognition, does not represent a serious implementation obstacle. An analysis of the overlap between the distributions of the decision variable shows the improved robustness of the proposed approach by means of a clear reduction of the classification error as the number of observations is increased. The proposed strategy is also compared to different VAD methods, including the G.729, AMR, and AFE standards, as well as recently reported algorithms showing a sustained advantage in speech/nonspeech detection accuracy and speech recognition performance.