José Manuel Ferrández-Vicente

Design and Implementation of a Highly Parameterised FPGA-Based Skeleton for Pairwise Biological Sequence Alignment

This paper describes a novel architecture for the hardware implementation of non-linear multi-layer cellular neural networks. This makes it feasible to design CNNs with millions of neurons accommodated in low price FPGA devices, being able to process standard video in real time.This paper presents the design and implementation of a generic and highly parameterised FPGA-based skeleton for pairwise biological sequence alignment. The skeleton is parameterised in terms of the sequence symbol type i.e. DNA, RNA, or protein sequences, the sequence lengths, the match score i.e. the score attributed to a symbol match or the penalty attributed to a mismatch or gap, and the matching task. Instances of the skeleton implement the Smith-Waterman and the Needleman-Wunsch algorithms. The skeleton has been captured in the Handel-C language which makes it FPGA-platform-independent. It implements the sequence alignment algorithm in hand using a pipeline of basic processing elements, which are tailored to the supplied parameters. Actual hardware implementations of the Smith-Waterman algorithm for protein sequence alignment achieve speed-ups in excess of 100:1 compared to equivalent standard desktop software implementations.

Neuromorphic detection of speech dynamics

Speech and voice technologies are experiencing a profound review as new paradigms are sought to overcome some specific problems which cannot be completely solved by classical approaches. Neuromorphic Speech Processing is an emerging area in which research is turning the face to understand the natural neural processing of speech by the Human Auditory System in order to capture the basic mechanisms solving difficult tasks in an efficient way. In the present paper a further step ahead is presented in the approach to mimic basic neural speech processing by simple neuromorphic units standing on previous work to show how formant dynamics - and henceforth consonantal features - can be detected by using a general neuromorphic unit which can mimic the functionality of certain neurons found in the upper auditory pathways. Using these simple building blocks a General Speech Processing Architecture can be synthesized as a layered structure. Results from different simulation stages are provided as well as a discussion on implementation details. Conclusions and future work are oriented to describe the functionality to be covered in the next research steps.

Simulating the phonological auditory cortex from vowel representation spaces to categories

Abstract Vowels are important clues supporting speech perception. Nevertheless in Computational Perception the definition of vowels is a very complex and elusive issue. The purpose of the present paper is to give a possible definition under the perceptual point of view. A vowel could be defined as an assignment of an acoustic–phonetic pattern to a specific categorical representation space. This assignment would be competitively instantiated in the cortical structures, depending on the specific phonological framework of the listeners language. An experimental framework is designed to test this definition on a Neuromorphic Speech Processing Architecture. Results from experiments to test reference patterns in Spanish, and possible extension to other languages with a larger repertoire of categories are presented and discussed.

Monitoring amyotrophic lateral sclerosis by biomechanical modeling of speech production

Neuromotor Degenerative Diseases (NDD) affecting mainly sub-thalamic and extra-pyramidal neuromotor structures leave significant marks in speech and phonation correlates. These may be used in the characterization, detection, grading and monitoring diseases and their progress in a non-invasive way. Considering that speech and phonation recording can be carried out using handy and low-cost instrumentation, speech and phonation correlates may be quite adequate candidates to define specific NDD biomarkers for disease progress monitoring protocols. The purpose of the paper is to present the fundamentals of speech articulation biomechanical modeling from the level of signal processing to neuromotor activity inference. This backward pathway involves several inverse problems, which are addressed separately. Results from study cases relevant in Amyotrophic Lateral Sclerosis are presented and discussed. The conclusions of the research show that several correlates may be reliably established, and that monitoring disease state and progress may rely on some biomechanical correlates informing on jaw and tongue neuromotor residual activity. Possible applications of the methodology to other neurodegenerative diseases are also discussed.

Low rate stochastic strategy for cochlear implants

In this article, a variant on low rate stochastic stimulation is presented. This variant shows more physiological characteristics, which are able to help preserving the cochlea due to low rate and low level stimulation. The speech processor here presented is provided with characteristics more similar to the normal peripheral auditory system. From the processors output, neural stimuli, it is possible to estimate both sound intensity per bands or areas of frequency, and the value of the frequencies composing the sound. When there is no sound stimulus, the peripheral auditory system generates stochastic stimuli from which it is not possible to extract information on amplitude neither frequency. In this case of clearly random generation of stimuli, the spectrum is noisy on the whole frequency.

Hand-based Interface for Augmented Reality

Augmented reality (AR) is a highly interdisciplinary field which has received increasing attention since late 90s. Basically, it consists of a combination of the real scene viewed by a user and a computer generated image, running in real time. So, AR allows the user to see the real world supplemented, in general, with some information considered as useful, enhancing the users perception and knowledge of the environment. Benefits of reconfigurable hardware for AR have been explored by Luk et al. [4]. However, the wide majority of AR systems have been based so far on PCs or workstations.

Relating Facial Myoelectric Activity to Speech Formants

Speech articulation is conditioned by the movements produced by well determined groups of muscles in the larynx, pharynx, mouth and face. The resulting speech shows acoustic features which are directly related with muscle neuromotor actions. Formants are some of the observable correlates most related to certain muscle actions, such as the ones activating jaw and tongue. As the recording of speech is simple and ubiquitous, the use of speech as a vehicular tool for neuromotor action monitoring would open a wide set of applications in the study of functional grading of neurodegenerative diseases. A relevant question is how far speech correlates and neuromotor action are related. This question is answered by the present study using electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. Correlation measurements help in establishing a clear relation between the time derivative of the first formant and the masseter myoelectric activity. Monitoring disease progress by acoustic kinematics in one case of Amyotrophic Lateral Sclerosis ALS is described.

Detection of Speech Dynamics by Neuromorphic Units

Speech and voice technologies are experiencing a profound review as new paradigms are sought to overcome some specific problems which can not be completely solved by classical approaches. Neuromorphic Speech Processing is an emerging area in which research is turning the face to understand the natural neural processing of speech by the Human Auditory System in order to capture the basic mechanisms solving difficult tasks in an efficient way. In the present paper a further step ahead is presented in the approach to mimic basic neural speech processing by simple neuromorphic units standing on previous work to show how formant dynamics -and henceforth consonantal features-, can be detected by using a general neuromorphic unit which can mimic the functionality of certain neurons found in the Upper Auditory Pathways. Using these simple building blocks a General Speech Processing Architecture can be synthesized as a layered structure. Results from different simulation stages are provided as well as a discussion on implementation details. Conclusions and future work are oriented to describe the functionality to be covered in the next research steps.

Neural Spike Activation in Hippocampal Cultures Using Hebbian Electrical Stimulation

Electric stimulation has been widely used to induce changes in neuronal cultures coupled to microelectrode arrays (MEAs). In this paper, we used different electrical stimulation protocols on dissociated cultures of hippocampal cells for studying the electrical properties of the process. We show that persistent and synchronous stimulation of adjacent electrodes may be used for creating adjacent physical or logical connections in the connectivity graph following Hebb’s Law modifying the neural responses principal parameters.

Neuromorphic detection of vowel representation spaces

In this paper a layered architecture to spot and characterize vowel segments in running speech is presented. The detection process is based on neuromorphic principles, as is the use of Hebbian units in layers to implement lateral inhibition, band probability estimation and mutual exclusion. Results are presented showing how the association between the acoustic set of patterns and the phonologic set of symbols may be created. Possible applications of this methodology are to be found in speech event spotting, in the study of pathological voice and in speaker biometric characterization, among others.