Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network

In this paper, we explore the capabilities of a sound classification system that combines both a novel FPGA cochlear model implementation and a bio-inspired technique based on a trained convolutional spiking network. The neuromorphic auditory system that is used in this work produces a form of representation that is analogous to the spike outputs of the biological cochlea. The auditory system has been developed using a set of spike-based processing building blocks in the frequency domain. They form a set of band pass filters in the spike-domain that splits the audio information in 128 frequency channels, 64 for each of two audio sources. Address Event Representation (AER) is used to communicate the auditory system with the convolutional spiking network. A layer of convolutional spiking network is developed and trained on a computer with the ability to detect two kinds of sound: artificial pure tones in the presence of white noise and electronic musical notes. After the training process, the presented system is able to distinguish the different sounds in real-time, even in the presence of white noise.Ministerio de Economía y Competitividad TEC2012-37868-C04-0

ED-Scorbot: A Robotic test-bed Framework for FPGA-based Neuromorphic systems

Neuromorphic engineering is a growing and promising discipline nowadays. Neuro-inspiration and brain understanding applied to solve engineering problems is boosting new architectures, solutions and products today. The biological brain and neural systems process information at relatively low speeds through small components, called neurons, and it is impressive how they connect each other to construct complex architectures to solve in a quasi-instantaneous way visual and audio processing tasks, object detection and tracking, target approximation, grasping…, etc., with very low power. Neuromorphs are beginning to be very promising for a new era in the development of new sensors, processors, robots and software systems that mimic these biological systems. The event-driven Scorbot (EDScorbot) is a robotic arm plus a set of FPGA / microcontroller’s boards and a library of FPGA logic joined in a completely event-based framework (spike-based) from the sensors to the actuators. It is located in Seville (University of Seville) and can be used remotely. Spike-based commands, through neuro-inspired motor controllers, can be sent to the robot after visual processing object detection and tracking for grasping or manipulation, after complex visual and audio-visual sensory fusion, or after performing a learning task. Thanks to the cascade FPGA architecture through the Address-Event-Representation (AER) bus, supported by specialized boards, resources for algorithms implementation are not limited.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

An Analog Neural Computer with Modular Architecture for Real-Time Dynamic Computations

The paper describes a multichip analog parallel neural network whose architecture, neuron characteristics, synaptic connections, and time constants are modifiable. The system has several important features, such as time constants for time-domain computations, interchangeable chips allowing a modifiable gross architecture, and expandability to any arbitrary size. Such an approach allows the exploration of different network architectures for a wide range of applications, in particular dynamic real-world computations. Four different modules (neuron, synapse, time constant, and switch units) have been designed and fabricated in a 2µm CMOS technology. About 100 of these modules have been assembled in a fully functional prototype neural computer. An integrated software package for setting the network configuration and characteristics, and monitoring the neuron outputs has been developed as well. The performance of the individual modules as well as the overall system response for several applications have been tested successfully. Results of a network for real-time decomposition of acoustical patterns will be discussed

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Neural architecture for echo suppression during sound source localization based on spiking neural cell models

Zusammenfassung Diese Arbeit untersucht die biologischen Ursachen des psycho-akustischen Präzedenz Effektes, der Menschen in die Lage versetzt, akustische Echos während der Lokalisation von Schallquellen zu unterdrücken. Sie enthält ein Modell zur Echo-Unterdrückung während der Schallquellenlokalisation, welches in technischen Systemen zur Mensch-Maschine Interaktion eingesetzt werden kann. Die Grundlagen dieses Modells wurden aus eigenen elektrophysiologischen Experimenten an der Mongolischen Wüstenrennmaus gewonnen. Die dabei erstmalig an der Wüstenrennmaus erzielten Ergebnisse, zeigen ein besonderes Verhalten spezifischer Zellen im Dorsalen Kern des Lateral Lemniscus, einer dedizierten Region des auditorischen Hirnstammes. Die dort sichtbare Langzeithemmung scheint die Grundlage für die Echounterdrückung in höheren auditorischen Zentren zu sein. Das entwickelte Model war in der Lage dieses Verhalten nachzubilden, und legt die Vermutung nahe, dass eine starke und zeitlich präzise Hyperpolarisation der zugrundeliegende physiologische Mechanismus dieses Verhaltens ist. Die entwickelte Neuronale Modellarchitektur modelliert das Innenohr und fünf wesentliche Kerne des auditorischen Hirnstammes in ihrer Verbindungsstruktur und internen Dynamik. Sie stellt einen neuen Typus neuronaler Modellierung dar, der als Spike-Interaktionsmodell (SIM) bezeichnet wird. SIM nutzen die präzise räumlich-zeitliche Interaktion einzelner Aktionspotentiale (Spikes) für die Kodierung und Verarbeitung neuronaler Informationen. Die Basis dafür bilden Integrate-and-Fire Neuronenmodelle sowie Hebb'sche Synapsen, welche um speziell entwickelte dynamische Kernfunktionen erweitert wurden. Das Modell ist in der Lage, Zeitdifferenzen von 10 mykrosekunden zu detektieren und basiert auf den Prinzipien der zeitlichen und räumlichen Koinzidenz sowie der präzisen lokalen Inhibition. Es besteht ausschließlich aus Elementen einer eigens entwickelten Neuronalen Basisbibliothek (NBL) die speziell für die Modellierung verschiedenster Spike- Interaktionsmodelle entworfen wurde. Diese Bibliothek erweitert die kommerziell verfügbare dynamische Simulationsumgebung von MATLAB/SIMULINK um verschiedene Modelle von Neuronen und Synapsen, welche die intrinsischen dynamischen Eigenschaften von Nervenzellen nachbilden. Die Nutzung dieser Bibliothek versetzt sowohl den Ingenieur als auch den Biologen in die Lage, eigene, biologisch plausible, Modelle der neuronalen Informationsverarbeitung ohne detaillierte Programmierkenntnisse zu entwickeln. Die grafische Oberfläche ermöglicht strukturelle sowie parametrische Modifikationen und ist in der Lage, den Zeitverlauf mikroskopischer Zellpotentiale aber auch makroskopischer Spikemuster während und nach der Simulation darzustellen. Zwei grundlegende Elemente der Neuronalen Basisbibliothek wurden zur Implementierung als spezielle analog-digitale Schaltungen vorbereitet. Erste Silizium Implementierungen durch das Team des DFG Graduiertenkollegs GRK 164 konnten die Möglichkeit einer vollparallelen on line Verarbeitung von Schallsignalen nachweisen. Durch Zuhilfenahme des im GRK entwickelten automatisierten Layout Generators wird es möglich, spezielle Prozessoren zur Anwendung biologischer Verarbeitungsprinzipien in technischen Systemen zu entwickeln. Diese Prozessoren unterscheiden sich grundlegend von den klassischen von Neumann Prozessoren indem sie räumlich und zeitlich verteilte Spikemuster, anstatt sequentieller binärer Werte zur Informationsrepräsentation nutzen. Sie erweitern das digitale Kodierungsprinzip durch die Dimensionen des Raumes (2 dimensionale Nachbarschaft) der Zeit (Frequenz, Phase und Amplitude) sowie der zeitlichen Dynamik analoger Potentialverläufe. Diese Dissertation besteht aus sieben Kapiteln, welche den verschiedenen Bereichen der Computational Neuroscience gewidmet sind. Kapitel 1 beschreibt die Motivation dieser Arbeit welche aus der Absicht rühren, biologische Prinzipien der Schallverarbeitung zu erforschen und für technische Systeme während der Interaktion mit dem Menschen nutzbar zu machen. Zusätzlich werden fünf Gründe für die Nutzung von Spike-Interaktionsmodellen angeführt sowie deren neuartiger Charakter beschrieben. Kapitel 2 führt die biologischen Prinzipien der Schallquellenlokalisation und den psychoakustischen Präzedenz Effekt ein. Aktuelle Hypothesen zur Entstehung dieses Effektes werden anhand ausgewählter experimenteller Ergebnisse verschiedener Forschungsgruppen diskutiert. Kapitel 3 beschreibt die entwickelte Neuronale Basisbibliothek und führt die einzelnen neuronalen Simulationselemente ein. Es erklärt die zugrundeliegenden mathematischen Funktionen der dynamischen Komponenten und beschreibt deren generelle Einsetzbarkeit zur dynamischen Simulation spikebasierter Neuronaler Netzwerke. Kapitel 4 enthält ein speziell entworfenes Modell des auditorischen Hirnstammes beginnend mit den Filterkaskaden zur Simulation des Innenohres, sich fortsetzend über mehr als 200 Zellen und 400 Synapsen in 5 auditorischen Kernen bis zum Richtungssensor im Bereich des auditorischen Mittelhirns. Es stellt die verwendeten Strukturen und Parameter vor und enthält grundlegende Hinweise zur Nutzung der Simulationsumgebung. Kapitel 5 besteht aus drei Abschnitten, wobei der erste Abschnitt die Experimentalbedingungen und Ergebnisse der eigens durchgeführten Tierversuche beschreibt. Der zweite Abschnitt stellt die Ergebnisse von 104 Modellversuchen zur Simulationen psycho-akustischer Effekte dar, welche u.a. die Fähigkeit des Modells zur Nachbildung des Präzedenz Effektes testen. Schließlich beschreibt der letzte Abschnitt die Ergebnisse der 54 unter realen Umweltbedingungen durchgeführten Experimente. Dabei kamen Signale zur Anwendung, welche in normalen sowie besonders stark verhallten Räumen aufgezeichnet wurden. Kapitel 6 vergleicht diese Ergebnisse mit anderen biologisch motivierten und technischen Verfahren zur Echounterdrückung und Schallquellenlokalisation und führt den aktuellen Status der Hardwareimplementierung ein. Kapitel 7 enthält schließlich eine kurze Zusammenfassung und einen Ausblick auf weitere Forschungsobjekte und geplante Aktivitäten. Diese Arbeit möchte zur Entwicklung der Computational Neuroscience beitragen, indem sie versucht, in einem speziellen Anwendungsfeld die Lücke zwischen biologischen Erkenntnissen, rechentechnischen Modellen und Hardware Engineering zu schließen. Sie empfiehlt ein neues räumlich-zeitliches Paradigma der dynamischen Informationsverarbeitung zur Erschließung biologischer Prinzipien der Informationsverarbeitung für technische Anwendungen.This thesis investigates the biological background of the psycho-acoustical precedence effect, enabling humans to suppress echoes during the localization of sound sources. It provides a technically feasible and biologically plausible model for sound source localization under echoic conditions, ready to be used by technical systems during man-machine interactions. The model is based upon own electro-physiological experiments in the mongolian gerbil. The first time in gerbils obtained results reveal a special behavior of specific cells of the dorsal nucleus of the lateral lemniscus (DNLL) - a distinct region in the auditory brainstem. The explored persistent inhibition effect of these cells seems to account for the base of echo suppression at higher auditory centers. The developed model proved capable to duplicate this behavior and suggests, that a strong and timely precise hyperpolarization is the basic mechanism behind this cell behavior. The developed neural architecture models the inner ear as well as five major nuclei of the auditory brainstem in their connectivity and intrinsic dynamics. It represents a new type of neural modeling described as Spike Interaction Models (SIM). SIM use the precise spatio-temporal interaction of single spike events for coding and processing of neural information. Their basic elements are Integrate-and-Fire Neurons and Hebbian synapses, which have been extended by specially designed dynamic transfer functions. The model is capable to detect time differences as small as 10 mircrosecondes and employs the principles of coincidence detection and precise local inhibition for auditory processing. It consists exclusively of elements of a specifically designed Neural Base Library (NBL), which has been developed for multi purpose modeling of Spike Interaction Models. This library extends the commercially available dynamic simulation environment of MATLAB/SIMULINK by different models of neurons and synapses simulating the intrinsic dynamic properties of neural cells. The usage of this library enables engineers as well as biologists to design their own, biologically plausible models of neural information processing without the need for detailed programming skills. Its graphical interface provides access to structural as well as parametric changes and is capable to display the time course of microscopic cell parameters as well as macroscopic firing pattern during simulations and thereafter. Two basic elements of the Neural Base Library have been prepared for implementation by specialized mixed analog-digital circuitry. First silicon implementations were realized by the team of the DFG Graduiertenkolleg GRK 164 and proved the possibility of fully parallel on line processing of sounds. By using the automated layout processor under development in the Graduiertenkolleg, it will be possible to design specific processors in order to apply theprinciples of distributed biological information processing to technical systems. These processors differ from classical von Neumann processors by the use of spatio temporal spike pattern instead of sequential binary values. They will extend the digital coding principle by the dimensions of space (spatial neighborhood), time (frequency, phase and amplitude) as well as the dynamics of analog potentials and introduce a new type of information processing. This thesis consists of seven chapters, dedicated to the different areas of computational neuroscience. Chapter 1: provides the motivation of this study arising from the attempt to investigate the biological principles of sound processing and make them available to technical systems interacting with humans under real world conditions. Furthermore, five reasons to use spike interaction models are given and their novel characteristics are discussed. Chapter 2: introduces the biological principles of sound source localization and the precedence effect. Current hypothesis on echo suppression and the underlying principles of the precedence effect are discussed by reference to a small selection of physiological and psycho-acoustical experiments. Chapter 3: describes the developed neural base library and introduces each of the designed neural simulation elements. It also explains the developed mathematical functions of the dynamic compartments and describes their general usage for dynamic simulation of spiking neural networks. Chapter 4: introduces the developed specific model of the auditory brainstem, starting from the filtering cascade in the inner ear via more than 200 cells and 400 synapses in five auditory regions up to the directional sensor at the level of the auditory midbrain. It displays the employed parameter sets and contains basic hints for the set up and configuration of the simulation environment. Chapter 5: consists of three sections, whereas the first one describes the set up and results of the own electro-physiological experiments. The second describes the results of 104 model simulations, performed to test the models ability to duplicate psycho-acoustical effects like the precedence effect. Finally, the last section of this chapter contains the results of 54 real world experiments using natural sound signals, recorded under normal as well as highly reverberating conditions. Chapter 6: compares the achieved results to other biologically motivated and technical models for echo suppression and sound source localization and introduces the current status of silicon implementation. Chapter 7: finally provides a short summary and an outlook toward future research subjects and areas of investigation. This thesis aims to contribute to the field of computational neuroscience by bridging the gap between biological investigation, computational modeling and silicon engineering in a specific field of application. It suggests a new spatio-temporal paradigm of information processing in order to access the capabilities of biological systems for technical applications

Intelligent Wireless Sensor Nodes for Human Footstep Sound Classification for Security Application

Sensor nodes present in a wireless sensor network (WSN) for security surveillance applications should preferably be small, energy-efficient and inexpensive with on-sensor computational abilities. An appropriate data processing scheme in the sensor node can help in reducing the power dissipation of the transceiver through compression of information to be communicated. In this paper, authors have attempted a simulation-based study of human footstep sound classification in natural surroundings using simple time-domain features. We used a spiking neural network (SNN), a computationally low weight classifier, derived from an artificial neural network (ANN), for classification. A classification accuracy greater than 85% is achieved using an SNN, degradation of ~5% as compared to ANN. The SNN scheme, along with the required feature extraction scheme, can be amenable to low power sub-threshold analog implementation. Results show that all analog implementation of the proposed SNN scheme can achieve significant power savings over the digital implementation of the same computing scheme and also over other conventional digital architectures using frequency-domain feature extraction and ANN-based classification.Comment: 12 pages, Journa

Neuromorphic audio processing through real-time embedded spiking neural networks.

In this work novel speech recognition and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound classification, including Deep Learning techniques. Based on these studies, novel audio processing and automatic audio signal recognition systems are proposed, using a bio-inspired auditory sensor as input. A desktop software tool called NAVIS (Neuromorphic Auditory VIsualizer) for post-processing the information obtained from spiking cochleae was implemented, allowing to analyze these data for further research. Next, using a 4-chip SpiNNaker hardware platform and Spiking Neural Networks, a system is proposed for classifying different time-independent audio signals, making use of a Neuromorphic Auditory Sensor and frequency studies obtained with NAVIS. To prove the robustness and analyze the limitations of the system, the input audios were disturbed, simulating extreme noisy environments. Deep Learning mechanisms, particularly Convolutional Neural Networks, are trained and used to differentiate between healthy persons and pathological patients by detecting murmurs from heart recordings after integrating the spike information from the signals using a neuromorphic auditory sensor. Finally, a similar approach is used to train Spiking Convolutional Neural Networks for speech recognition tasks. A novel SCNN architecture for timedependent signals classification is proposed, using a buffered layer that adapts the information from a real-time input domain to a static domain. The system was deployed on a 48-chip SpiNNaker platform. Finally, the performance and efficiency of these systems were evaluated, obtaining conclusions and proposing improvements for future works.Premio Extraordinario de Doctorado U

An application of an auditory periphery model in speaker identification

The number of applications of automatic Speaker Identification (SID) is growing due to the advanced technologies for secure access and authentication in services and devices. In 2016, in a study, the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlear model achieved the best performance among seven recent cochlear models to fit a set of human auditory physiological data. Motivated by the performance of the CAR-FAC, I apply this cochlear model in an SID task for the first time to produce a similar performance to a human auditory system. This thesis investigates the potential of the CAR-FAC model in an SID task. I investigate the capability of the CAR-FAC in text-dependent and text-independent SID tasks. This thesis also investigates contributions of different parameters, nonlinearities, and stages of the CAR-FAC that enhance SID accuracy. The performance of the CAR-FAC is compared with another recent cochlear model called the Auditory Nerve (AN) model. In addition, three FFT-based auditory features – Mel frequency Cepstral Coefficient (MFCC), Frequency Domain Linear Prediction (FDLP), and Gammatone Frequency Cepstral Coefficient (GFCC), are also included to compare their performance with cochlear features. This comparison allows me to investigate a better front-end for a noise-robust SID system. Three different statistical classifiers: a Gaussian Mixture Model with Universal Background Model (GMM-UBM), a Support Vector Machine (SVM), and an I-vector were used to evaluate the performance. These statistical classifiers allow me to investigate nonlinearities in the cochlear front-ends. The performance is evaluated under clean and noisy conditions for a wide range of noise levels. Techniques to improve the performance of a cochlear algorithm are also investigated in this thesis. It was found that the application of a cube root and DCT on cochlear output enhances the SID accuracy substantially