scholarly journals FINANCIAL SIGNAL PROCESSING: A SELF CALIBRATING MODEL

2001 ◽  
Vol 04 (04) ◽  
pp. 567-584 ◽  
Author(s):  
ROBERT J. ELLIOTT ◽  
WILLIAM C. HUNTER ◽  
BARBARA M. JAMIESON
Keyword(s):  
Signal Processing ◽  
Markov Chain ◽  
Term Structure ◽  
Markov Models ◽  
Estimation Procedure ◽  
Rate Process ◽  
Treasury Bills ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Selection Of

Previous work on multifactor term structure models has proposed that the short rate process is a function of some unobserved diffusion process. We consider a model in which the short rate process is a function of a Markov chain which represents the "state of the world". This enables us to obtain explicit expressions for the prices of zero-coupon bonds and other securities. Discretizing our model allows the use of signal processing techniques from Hidden Markov Models. This means we can estimate not only the unobserved Markov chain but also the parameters of the model, so the model is self-calibrating. The estimation procedure is tested on a selection of U.S. Treasury bills and bonds.

Speech Disorders Recognition using Speech Analysis

2013 ◽  
pp. 494-507 ◽  
Author(s):  
Khaled Necibi ◽  
Halima Bahi ◽  
Toufik Sari
Keyword(s):  
Signal Processing ◽  
Hidden Markov Models ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Speech Disorders ◽  
Speech Analysis ◽  
Physical Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Gert Van Dijck ◽  
Marc M. Van Hulle
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Cell Types ◽  
Signal Quality ◽  
Quality Signals ◽  
Processing Techniques ◽  
Electrode Selection ◽  
Signal Processing Techniques ◽  
Processing Steps ◽  
Selection Of

AbstractRecently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”

Speech Disorders Recognition using Speech Analysis

2012 ◽  
pp. 310-324
Author(s):  
Khaled Necibi ◽  
Halima Bahi ◽  
Toufik Sari
Keyword(s):  
Signal Processing ◽  
Hidden Markov Models ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Speech Disorders ◽  
Speech Analysis ◽  
Physical Problems ◽  
Processing Techniques ◽  
Signal Processing Techniques

Speech disorders are human disabilities widely present in young population but also adults may suffer from such disorders after some physical problems. In this context, the detection and further the correction of such disabilities may be handled by Automatic Speech Recognition (ASR) technology. The first works on the speech disorders detection began early in the 70s and seem to follow the same evolution as those on the ASR. Indeed, these early works were more based on the signal processing techniques. Progressively, systems dealing with speech disorders incorporate more ideas from ASR technology. Particularly, Hidden Markov Models, the state-of-the-art approaches in ASR systems, are used. This chapter reviews systems that use ASR techniques to evaluate pronunciation of people who suffer from speech or voice impairments. The authors investigate the existing systems and present the main innovation and some of the available resources.

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