Maria Marinaro

Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks

We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (vt). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (mlp) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the mlp network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude vt events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (vt versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications.

Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm

In this paper a neural network for approximating continuous and discontinuous mappings is described. The activation functions of the hidden nodes are the Radial Basis Functions (RBF) whose variances are learnt by means of an evolutionary optimization strategy. A new incremental learning strategy is used in order to improve the net performances. The learning strategy is able to save computational time because of the selective growing of the net structure and the capability of the learning algorithm to keep the effects of the activation functions local. Further, it does not require high order derivatives. An analysis of the learning capabilities and a comparison of the net performances with other approaches reported in literature have been performed. It is shown that the resulting network improves the approximation results reported for continuous mappings and for those exhibiting a finite number of discontinuities.

Discrimination of Earthquakes and Underwater Explosions Using Neural Networks

We report on the implementation of an automatic system able to discriminate between explosion-generated artificial seismic events and local earthquakes in the Phlegraean Fields (Italy). The explosions are fired weekly at the sea bottom (tens of meters below sea level) by fishermen in Pozzuoli bay; earthquakes are volcano-tectonic quakes with depths shallower than 4 km. The discrimination system is based on an artificial neural network and is composed of two modules. The first is devoted to the extraction of the seismogram signatures and the second to the classification of the seismic events into two classes. For the features extraction (preprocessing stage), instead of the conventional Fourier spectral analysis, we use a Linear Prediction Coding (LPC) algorithm. This approach compresses the data from 256 samples to only 7 parameters and can extract robust features for the spectrogram representation. The classification is performed using a supervised neural algorithm based on a Multilayer Neural Network (MLP) architecture. We applied the method to a set of 30 seismic events recorded by the stations of the local seismic network, 15 of which were generated by the fishermens explosions and 15 were volcano-tectonic earthquakes. We dealt with a total of 280 records from different stations, 121 relating to explosions and 159 to earthquakes. Data were divided in a training set containing 120 traces for earthquakes and 90 for explosions, and a test set containing 70 traces corresponding to 39 records for earthquakes and 31 records for explosions. On the test set the neural net gave a classification performance of 92%, indicating a good ability of the net to generalize. Manuscript received 10 January 2002.

Verbal and Nonverbal Communication Behaviours

COST 2102: Cross-Modal Analysis of Verbal and Nonverbal Communication (CAVeNC).- I - Verbal and Noverbal Coding Schema.- Annotation Schemes for Verbal and Non-Verbal Communication: Some General Issues.- Presenting in Style by Virtual Humans.- Analysis of Nonverbal Involvement in Dyadic Interactions.- II - Emotional Expressions.- Childrens Perception of Musical Emotional Expressions.- Emotional Style Conversion in the TTS System with Cepstral Description.- Meaningful Parameters in Emotion Characterisation.- III - Gestural Expressions.- Prosodic and Gestural Expression of Interactional Agreement.- Gesture, Prosody and Lexicon in Task-Oriented Dialogues: Multimedia Corpus Recording and Labelling.- Egyptian Grunts and Transportation Gestures.- IV - Analysis and Algorithms for Verbal and Nonverbal Speech.- On the Use of NonVerbal Speech Sounds in Human Communication.- Speech Spectrum Envelope Modeling.- Using Prosody in Fixed Stress Languages for Improvement of Speech Recognition.- Single-Channel Noise Suppression by Wavelets in Spectral Domain.- Voice Source Change During Fundamental Frequency Variation.- A Gesture-Based Concept for Speech Movement Control in Articulatory Speech Synthesis.- A Novel Psychoacoustically Motivated Multichannel Speech Enhancement System.- Analysis of Verbal and Nonverbal Acoustic Signals with the Dresden UASR System.- V - Machine Multimodal Interaction.- VideoTRAN: A Translation Framework for Audiovisual Face-to-Face Conversations.- Spoken and Multimodal Communication Systems in Mobile Settings.- Multilingual Augmentative Alternative Communication System.- Analysis and Synthesis of Multimodal Verbal and Non-verbal Interaction for Animated Interface Agents.- Generating Nonverbal Signals for a Sensitive Artificial Listener.- Low-Complexity Algorithms for Biometric Recognition.- Towards to Mobile Multimodal Telecommunications Systems and Services.- Embodied Conversational Agents in Wizard-of-Oz and Multimodal Interaction Applications.- Telling Stories with a Synthetic Character: Understanding Inter-modalities Relations.

Multimodal Signals: Cognitive and Algorithmic Issues

Interactive and Unsupervised Multimodal Systems.- Multimodal Human Machine Interactions in Virtual and Augmented Reality.- Speech through the Ear, the Eye, the Mouth and the Hand.- Multimodality Issues in Conversation Analysis of Greek TV Interviews.- Representing Communicative Function and Behavior in Multimodal Communication.- Using the iCat as Avatar in Remote Meetings.- Using Context to Disambiguate Communicative Signals.- Modeling Aspects of Multimodal Lithuanian Human - Machine Interface.- Using a Signing Avatar as a Sign Language Research Tool.- Data Fusion at Different Levels.- Voice Technology Applied for Building a Prototype Smart Room.- Towards Facial Gestures Generation by Speech Signal Analysis Using HUGE Architecture.- Multi-modal Speech Processing Methods: An Overview and Future Research Directions Using a MATLAB Based Audio-Visual Toolbox.- From Extensity to Protensity in CAS: Adding Sounds to Icons.- Statistical Modeling of Interpersonal Distance with Range Imaging Data.- Verbal and Nonverbal Communication Signals.- How the Brain Processes Language in Different Modalities.- From Speech and Gestures to Dialogue Acts.- The Language of Interjections.- Gesture and Gaze in Persuasive Political Discourse.- Content in Embedded Sentences.- A Distributional Concept for Modeling Dialectal Variation in TTS.- Regionalized Text-to-Speech Systems: Persona Design and Application Scenarios.- Vocal Gestures in Slovak: Emotions and Prosody.- Spectrum Modification for Emotional Speech Synthesis.- Comparison of Grapheme and Phoneme Based Acoustic Modeling in LVCSR Task in Slovak.- Automatic Motherese Detection for Face-to-Face Interaction Analysis.- Recognition of Emotions in German Speech Using Gaussian Mixture Models.- Electroglottogram Analysis of Emotionally Styled Phonation.- Emoticonsciousness.- Urban Environmental Information Perception and Multimodal Communication: The Air Quality Example.- Underdetermined Blind Source Separation Using Linear Separation System.- Articulatory Synthesis of Speech and Singing: State of the Art and Suggestions for Future Research.- Qualitative and Quantitative Crying Analysis of New Born Babies Delivered Under High Risk Gestation.- Recognizing Facial Expressions Using Model-Based Image Interpretation.- Face Localization in 2D Frontal Face Images Using Luminosity Profiles Analysis.

Unsupervised Neural Analysis of Very-Long-Period Events at Stromboli Volcano Using the Self-Organizing Maps

We have implemented a method based on an unsupervised neural network to cluster the waveforms of very-long-period (VLP) events associated with explosive activity at the Stromboli volcano (southern Italy). Stromboli has several active vents in the summit area producing together more than 200 explosions/day. We applied this method to investigate the relationship between each vent and its associated VLP explosive waveform. We selected 147 VLP events recorded between November and December 2005, when digital infrared camera recordings were available. From a visual inspection of the infrared camera images, we classified the VLPs on the basis of which vent produced each explosion. We then applied the self-organizing map (SOM), an unsupervised neural technique widely applied in data exploratory analysis, to cluster the VLPs on the basis of their waveform similarity. Our analysis demonstrates that the most recurrent VLP waveforms are usually generated by the same vent. Some exceptions occurred, however, in which different waveforms are associated with the same vent, as well as different vents generating similar waveforms. This suggests that the geometry of the upper conduit-vent system plays a role in shaping the recurring VLP events, whereas occasional modest changes in the source process dynamics produce the observed exceptions.

Zero-conductance resonances and spin filtering effects in ring conductors subject to Rashba coupling

We investigate the effect of Rashba spin-orbit coupling and of a tunnel barrier on the zero-conductance resonances appearing in a one-dimensional conducting Aharonov-Bohm (AB) ring symmetrically coupled to two leads. The transmission function of the corresponding one-electron problem is derived within the scattering matrix approach and analyzed in the complex energy plane with focus on the role of the tunnel barrier strength on the zero-pole structure characteristic of transmission (anti)resonances. The lifting of the real conductance zeros is related to the breaking of the spin-reversal symmetry and time-reversal symmetry of Aharonov-Casher and AB rings, as well as to rotational symmetry breaking in the presence of a tunnel barrier. We show that the polarization direction of transmitted electrons can be controlled via the tunnel barrier strength and discuss a possible spin-filtering design in one-dimensional rings with tunable spin-orbit interaction.

Automatic Discrimination among Landslide, Explosion-Quake, and Microtremor Seismic Signals at Stromboli Volcano Using Neural Networks

In this article we report on the implementation of an automatic system for discriminating landslide seismic signals on Stromboli island (southern Italy). This is a critical point for monitoring the evolution of this volcanic island, where at the end of 2002 a violent tsunami occurred, triggered by a big landslide. We have devised a supervised neural system to discriminate among landslide, explosion-quake, and volcanic microtremor signals. We first preprocess the data to obtain a compact representation of the seismic records. Both spectral features and amplitude-versus-time information have been extracted from the data to characterize the different types of events. As a second step, we have set up a supervised classification system, trained using a subset of data (the training set) and tested on another data set (the test set) not used during the training stage. The automatic system that we have realized is able to correctly classify 99% of the events in the test set for both explosion-quake/ landslide and explosion-quake/microtremor couples of classes, 96% for landslide/ microtremor discrimination, and 97% for three-class discrimination (landslides/ explosion-quakes/microtremor). Finally, to determine the intrinsic structure of the data and to test the efficiency of our parametrization strategy, we have analyzed the preprocessed data using an unsupervised neural method. We apply this method to the entire dataset composed of landslide, microtremor, and explosion-quake signals. The unsupervised method is able to distinguish three clusters corresponding to the three classes of signals classified by the analysts, demonstrating that the parametrization technique characterizes the different classes of data appropriately.

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and α quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonens self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results.

Nonlinear Speech Modeling and Applications

Dealing with Nonlinearities in Speech Signals.- Some Notes on Nonlinearities of Speech.- Nonlinear Speech Processing: Overview and Possibilities in Speech Coding.- Signal Processing in a Nonlinear, NonGaussian, and Nonstationary World.- Acoustic-to-Articulatory Modeling of Speech Phenomena.- The Analysis of Voice Quality in Speech Processing.- Identification of Nonlinear Oscillator Models for Speech Analysis and Synthesis.- Speech Modelling Based on Acoustic-to-Articulatory Mapping.- Data Driven and Speech Processing Algorithms.- Underdetermined Blind Separation of Speech Signals with Delays in Different Time-Frequency Domains.- Data Driven Approaches to Speech and Language Processing.- Cepstrum-Based Harmonics-to-Noise Ratio Measurement in Voiced Speech.- Predictive Connectionist Approach to Speech Recognition.- Modeling Speech Based on Harmonic Plus Noise Models.- Algorithms and Models Based on Speech Perception Mechanisms.- Text Independent Methods for Speech Segmentation.- Nonlinear Adaptive Speech Enhancement Inspired by Early Auditory Processing.- Perceptive, Non-linear Speech Processing and Spiking Neural Networks.- Task Oriented Speech Applications.- An Algorithm to Estimate Anticausal Glottal Flow Component from Speech Signals.- Non-linear Speech Feature Extraction for Phoneme Classification and Speaker Recognition.- Segmental Scores Fusion for ALISP-Based GMM Text-Independent Speaker Verification.- On the Usefulness of Almost-Redundant Information for Pattern Recognition.- An Audio-Visual Imposture Scenario by Talking Face Animation.- Cryptographic-Speech-Key Generation Using the SVM Technique over the lp-Cepstral Speech Space.- Nonlinear Speech Features for the Objective Detection of Discontinuities in Concatenative Speech Synthesis.- Signal Sparsity Enhancement Through Wavelet Transforms in Underdetermined BSS.- A Quantitative Evaluation of a Bio-inspired Sound Segregation Technique for Two- and Three-Source Mixtures.- Application of Symbolic Machine Learning to Audio Signal Segmentation.- Analysis of an Infant Cry Recognizer for the Early Identification of Pathologies.- Graphical Models for Text-Independent Speaker Verification.- An Application to Acquire Audio Signals with ChicoPlus Hardware.- Speech Identity Conversion.- Robust Speech Enhancement Based on NPHMM Under Unknown Noise.