Carmen Benítez

Volcano-seismic signal detection and classification processing using hidden Markov models. Application to San Cristóbal volcano, Nicaragua

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 carried out in February to March 2006. More than 600 hours of data in San Cristobal volcano were analyzed and 1098 seismic events were registered at short period stations. These events were manually labelled by a single expert technicians and identified three types classes of signals (S1, S2, S3) and tremor background seismic noise (NS). The method analyzes the seismograms comparing the characteristics of the data to a number of event classes defined beforehand. If a signal is present, the method detects its occurrence and produces a classification. From the application performed over our data set, we have demonstrated that in order to have a reliable result, a careful and adequate segmentation process is crucial. Also, each type of signals requires its own characterization. That is, each signal type must be represented by its own specific model, which would include the effects of source, path and sites. Once we have built this model, the success level of the system is high. Extensive performance evaluation is conducted to derive the optimal configuration of the different parameters Correct classification rates of up to 80% are achieved. The high success rates obtained imply that the method is fully able to detect, isolate, and identify seismic signals on raw seismic data. These results imply that, once an adequate training process has been used, the present method is particularly appropriate to work in real time, and in parallel to the data acquisition.

Discriminative Feature Selection for Automatic Classification of Volcano-Seismic Signals

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.

Histogram Equalization for Robust Speech Recognition

Optimal Automatic Speech Recognition takes place when the evaluation is done under circumstances identical to those in which the recognition system was trained. In the speech applications demanded in the actual real world this will almost never happen. There are several variability sources which produce mismatches between the training and test conditions. Depending on his physical or emotional state, a speaker will produce sounds with unwanted variations transmitting no acoustic relevant information. The phonetic context of the sounds produced will also introduce undesired variations. Inter-speaker variations must be added to those intra-speaker variations. They are related to the peculiarities of speakers’ vocal track, his gender, his socio-linguistic environment, etc. A third source of variability is constituted by the changes produced in the speaker’s environment and the characteristics of the channel used to communicate. The strategies used to eliminate the group of environmental sources of variation are called

Speech Recognition Under Noise Conditions: Compensation Methods

In most of the practical applications of Automatic Speech Recognition (ASR), the input speech is contaminated by a background noise. This strongly degrades the performance of speech recognizers (Gong, 1995; Cole et al., 1995; Torre et al., 2000). The reduction of the accuracy could make unpractical the use of ASR technology in applications that must work in real conditions, where the input speech is usually affected by noise. For this reason, robust speech recognition has become an important focus area of speech research (Cole et al., 1995).Noise has two main effects over the speech representation: it introduces a distortion in the representation space, and it also causes a loss of information, due to its random nature. The distortion of the representation space due to the noise causes a mismatch between the training (clean) and recognition (noisy) conditions. The acoustic models, trained with speech acquired under clean conditions do not model speech acquired under noisy conditions accurately and this degrades the performance of speech recognizers. Most of the methods for robust speech recognition are mainly concerned with the reduction of this mismatch. On the other hand, the information loss caused by noise introduces a degradation even in the case of an optimal mismatch compensation.In this chapter we analyze the problem of speech recognition under noise conditions. Firstly, we study the effect of the noise over the speech representation and over the recognizer performance. Secondly, we consider two categories of methods for compensating the effect of noise over the speech representation. The first one performs a model-based compensation formulated in a statistical framework. The second one considers the main effect of the noise as a transformation of the representation space and compensates the effect of the noise by applying the inverse transformation.

Database of multi-parametric geophysical data from the TOMO-DEC experiment on Deception Island, Antarctica

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 five land-based locations on Deception Island (the Spanish scientific Antarctic base ‘Gabriel de Castilla’ and four temporary camps). This experiment allowed us to record active seismic signals using a large network of seismic stations that were deployed both on land and on the seafloor. In addition, other geophysical data were acquired, including bathymetric high precision multi-beam data, and gravimetric and magnetic profiles. To date, the seismic and bathymetric data have been analysed but the magnetic and gravimetric data have not. We provide P-wave arrival-time picks and seismic tomography results in velocity and attenuation. In this manuscript, we describe the main characteristics of the experiment, the instruments, the data, and the repositories from which data and information can be obtained.