
Carmen BenitezUniversity of Granada | UGR · Department of Signal Theory, Telematics and Communications
Carmen Benitez
Doctor of Philosophy
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110
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Introduction
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January 1999 - July 2013
Publications
Publications (110)
Introduction: Volcano seismology has successfully predicted several eruptions and includes many reliable methods that have been adopted extensively by volcanic observatories; however, there are several problems that still lack solutions. Meanwhile, the overwhelming success of data-driven models to solve predictive complex real-world problems positi...
Real-time monitoring of volcano-seismic signals is complex. Typically, automatic systems are built by learning from large seismic catalogues, where each instance has a label indicating its source mechanism. However, building complete catalogues is difficult owing to the high cost of data-labelling. Current machine learning techniques have achieved...
Monitoring continuous volcano-seismic signals is often performed by systems trained on scarce or incomplete datasets. From a machine learning perspective, these types of systems are typically built by learning from seismic records containing information not only on the volcanic dynamics, but also on the complex inner structure of the volcanic edifi...
Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at differe...
The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava...
The search for pre‐eruptive observables that can be used for short‐term volcanic forecast remains a scientific challenge. Pre‐eruptive patterns in seismic data are usually identified by analyzing seismic catalogs (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcan...
In this work we demonstrate that Shannon Entropy (SE) calculated on continuous seismic signals can be used efficiently in a volcanic monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and...
The search for pre-eruptive observables that can be used for short-term volcanic early warning remains a scientific challenge. Pre-eruptive patterns in seismic data are usually identified by analyzing seismic catalogues (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the...
Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the ex...
Deep Learning has advanced seismo-volcanic monitoring to unprecedented performance levels. Nevertheless, seismic data labeling still requires substantial annotation efforts, often delayed in time if the eruptive state alters the data conditions. The selective segmentation of which earthquake transients have to be reviewed by an expert can significa...
Methods for volcano monitoring that are based on analysis of geophysical data often rely on deterministic approaches without considering the complex and dynamic nature of volcanic systems. To detect subtle changes within seismic sequences associated with volcanic unrest, specialized workflows for data classification and analysis are required. Here,...
We introduce an end-to-end (E2E) deep neural network architecture designed to perform seismo-volcanic monitoring focused on detecting change. Due to the complexity of volcanic processes, this requires a polyphonic detection, segmentation, and classification approach. Through evolving epistemic uncertainty, invoking a Bayesian network strategy, we d...
Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic...
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 overfi...
Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known cla...
Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL;...
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...
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...
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 ev...
Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based o...
Deception Island volcano (Antarctica) is one of the most closely monitored and studied volcanoes on the region. In January 2005, a multi-parametric international experiment was conducted that encompassed both Deception Island and its surrounding waters. We performed this experiment from aboard the Spanish oceanographic vessel ‘Hespérides’, and from...
This work presents a novel approach to automatic detection of long period events (LP) in continuous seismic records. Without any supervised learning, the proposal is based on a simple processing to search for the LP characteristic shape, duration, and band of activity. Continuous raw signals from the seismometer are first filtered into three freque...
In the present paper we describe the on-land field operations integrated in the TOMO-ETNA experiment carried out in June-November 2014 at Mt. Etna volcano and surrounding areas. This terrestrial campaign consists in the deployment of 90 short-period portable three-component seismic stations, 17 broadband seismometers and the coordination with 133 p...
This work describes the automatic picking of the P-phase arrivals of the 3*106 seismic registers originated during the TOMO-ETNA experiment. Air-gun shots produced by the vessel Sarmiento de Gamboa and contemporary passive seismicity occurring in the island are recorded by a dense network of stations deployed for the experiment. In such scenario, a...
In this manuscript we present the new friendly seismic tomography software based on joint inversion of active and passive seismic sources called PARTOS (Passive Active Ray Tomography Software). This code has been developed on the base of two well-known widely used tomographic algorithms (LOTOS and ATOM-3D), providing a robust set of algorithms. The...
The accurate estimation of the arrival time of seismic waves or picking is a problem of major interest in seismic research given its relevance in many seismological applications, such as earthquake source location and active seismic tomography. In the last decades, several automatic picking methods have been proposed with the ultimate goal of imple...
A prototype of phonetics learning application based on neurofeedback is presented. The learner's auditive discrimination of chosen phonemes is evaluated using his EEG response to auditory contrasts in an oddball paradigm experiment. When auditory contrasts distinction takes place, the well-known mismatch negativity (MMN) potential registered presen...
This work proposes a procedure to measure the human capability to discriminate couples of English vocalic phonemes embedded into words. Using the analysis of the EEG response to auditory contrasts in an oddball paradigm experiment, the Medium Mismatch Negativity potential (MMN) is evaluated. When the discrimination is achieved, MMN has a negative a...
This paper describes a novel framework to sub-band based Histogram Equalization (HEQ) applied to robust speech recognition. We propose a frequency band specific equalization to compensate the noise distortion on the individual frequency bands. The proposed equalization framework is a two step process. In the first step, conventional histogram equal...
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 37...
Seismic activity is one of the main precursors of volcanic eruptions as it usually increases before crises. The material Failure Forecast Method (FFM) is the most common approach used for eruption prediction. It is based on empirical power laws applied to observables which can be the mean level of seismic signal, the rate of occurrence or the energ...
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...
This paper proposes TELIAMADE (an indoor location system based on ultrasonic and radiofrequency signals) to be used as a teaching tool in the context of Telecommunication Engineering. Due to its simple design, the versatility of its configuration and the characteristics of the involved signals, TELIAMADE is an appropriate tool for teaching basic as...
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 L...
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 obtaine...
Beginning in July 2003 and lasting through September 2003, the Norris
Geyser Basin in Yellowstone National Park exhibited an unusual increase
in ground temperature and hydrothermal activity. Using hidden Markov
model theory, we identify over five million high-frequency
(>15 Hz) seismic events observed at a temporary seismic station
deployed in the...
This paper describes a novel modification of Histogram Equalization (HEQ) approach to robust speech recognition. We propose separate equalization of the high frequency (HF) and low frequency (LF) bands. We study different combinations of the sub-band equalization and obtain best results when we perform a two-stage equalization. First, conventional...
In this paper, we describe a computationally efficient approach for combining speaker and noise normalization techniques. In particular, we combine the simple yet effective Histogram Equalization (HEQ) for noise compensation with Vocal-tract length normalization (VTLN) for speaker-normalization. While it is intuitive to remove noise first and then...
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...
On 19 March, 2008 eruptive activity returned to the summit of Kilauea Volcano, Hawai`i with the formation of a new vent within the Halema`uma`u pit crater. The new vent has been gradually increasing in size, and exhibiting sustained degassing and the episodic bursting of gas slugs at the surface of a lava pond ~200 m below the floor of Halema`uma`u...
On 19 March, 2008 eruptive activity returned to the summit of Kilauea Volcano, Hawaii with the formation of a new vent within the Halemaumau pit crater. The new vent has been gradually increasing in size, and exhibiting sustained degassing and the episodic bursting of gas slugs at the surface of a lava pond ∼200 m below the floor of Halemaumau. The...
The aim of this work is to apply the Hidden Markov Model (HMM) method to recognise seismic signals belonging to different active volcanoes. We use data obtained from two field surveys carried out in 1997 and 1999 at Stromboli and Etna, respectively. For Stromboli we used two types of seismic signals for recognition purposes: Strombolian explosions...
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 Freque...
We present a method for automatic seismic event detection and classification, focusing on volcanic-seismic signals by means of the validity of the hidden Markov modeling (HMM) method in active volcanoes. Recordings of different seismic event types are studied at one active volcano; San Cristobal in Nicaragua. We use data from one field surveys carr...
Ed. Bean, Braiden, Lockmer, Martini and O’Brien. The VOLUME project. VOLcanoes: Understanding subsurface mass movement. Printed by jaycee. ISBN: 978-1-905254-39-2, pp 130-139.
The goal of the present work is to apply of the Hidden Markov Model (HMM) method to recognize signals belonging to different active volcanoes. We use data from two field surveys carried out in 1997 and 1999 at Stromboli and Etna, respectively. For Stromboli volcano we define two types of seismic signals to recognize: Strombolian explosions and back...
We present a method for automatic seismic event detection and classification, focusing on volcanic-seismic signals by means of the validity of the hidden Markov modeling (HMM) method in active volcanoes. Recordings of different seismic event types are studied at two active volcanoes; Telica and San Cristóbal in Nicaragua. We use data from two field...
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 or...
After analyzing the effects of additive noise on the speech recognition, this chapter has described the Histogram Equalization as the main representative of the statistical matching normalization strategies for automatic speech recognition. Its main attractive is the low computational cost added to the advantage of not needing any noise model or SN...
In this chapter, we have presented an overview of methods for noise robust speech recognition and a detailed description of the mechanism degrading the performance of speech recognizers working under noise conditions. Performance is degraded because of the mismatch between training and recognition and also because of the information loss associated...
This paper shows a complete seismic-event classification and monitoring system that has been developed based on the seismicity observed during three summer Antarctic surveys at the Deception Island Volcano, Antarctica. The system is based on the state of the art in hidden Markov modeling (HMM) techniques successfully applied to other scenarios. A d...
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 techn...
A filter that introduces inter-frame information into the voice fea-tures set is proposed in this paper. The filter adds the autocor-relations 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 in...
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 SN...
This paper shows a complete seismic event classification system based on the state of the art in Hidden Markov Modeling (HMM) which has been successfully used for discriminating between different seismic events produced at active volcanoes. A database consisting of a representative set of different seismic events including explosions and tremor bur...
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...
This paper shows a complete volcano monitoring system that has been developed on the basis of the seismicity observed during three summer Antarctic surveys at Deception Island Volcano (Antarctica). The system is based on the state of the art in hidden Markov modelling (HMM) techniques successfully applied to other scenarios. A database containing a...
In this paper we present some results from a net-like structure for Hidden Markov Models, applied to speech recognition. Net topology is a Recurrent Neural Network in which each temporary step is identified as a layer. Backpropagation techniques are used to train the RNN-HMM. Two types of training estimations are used: Maximum Likelihood and Compet...
RESUMEN En este trabajo proponemos un detector de actividad de voz (VAD) derivado de la aproximación VTS (Vector Tay-lor Series approach). Se hace uso de una mezcla de Gaus-sianas (entrenada con voz limpia) para proporcionar una decisión apropiada para la detección de voz/no-voz. La aproximación VTS adapta la mezcla de Gaussianas a las condiciones...
This paper shows a revised statistical test for voice activi- ty detection in noise adverse environments. The method is based on a revised contextual likelihood ratio test (LRT) defined over a multiple observation window. The new ap- proach not only evaluates the two hypothesis consisting on all the observations to be speech or non-speech but all t...
The objective of this work is to build a prototype of an automatic system of recognition and classification of seismic events in active volcanoes. Capable in a future of working in real time and this way, facilitate the work of the specialists in the analysis and future predictions that happen in the development of the activity of a volcano. The de...
An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on the determination of the speech/nonspeech divergence by means of specialized order statistics filters (OSFs) working on the subband log-energies. This algorithm differs from many others in th...
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 algorit...