158 research outputs found
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
Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Auscultation is one of the most used techniques for
detecting cardiovascular diseases, which is one of the main causes
of death in the world. Heart murmurs are the most common abnormal
finding when a patient visits the physician for auscultation.
These heart sounds can either be innocent, which are harmless, or
abnormal, which may be a sign of a more serious heart condition.
However, the accuracy rate of primary care physicians and expert
cardiologists when auscultating is not good enough to avoid most
of both type-I (healthy patients are sent for echocardiogram) and
type-II (pathological patients are sent home without medication or
treatment) errors made. In this paper, the authors present a novel
convolutional neural network based tool for classifying between
healthy people and pathological patients using a neuromorphic
auditory sensor for FPGA that is able to decompose the audio into
frequency bands in real time. For this purpose, different networks
have been trained with the heart murmur information contained in
heart sound recordings obtained from nine different heart sound
databases sourced from multiple research groups. These samples
are segmented and preprocessed using the neuromorphic auditory
sensor to decompose their audio information into frequency
bands and, after that, sonogram images with the same size are
generated. These images have been used to train and test different
convolutional neural network architectures. The best results
have been obtained with a modified version of the AlexNet model,
achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%,
PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid
cardiologists and primary care physicians in the auscultation process,
improving the decision making task and reducing type-I and
type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-
NAVIS: Neuromorphic Auditory VISualizer Tool
This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).Ministerio de Economía y Competitividad TEC2012-37868-C04-0
Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)
In stereo-vision processing, the image-matching step is essential for results, although it
involves a very high computational cost. Moreover, the more information is processed, the more time
is spent by the matching algorithm, and the more ine cient it is. Spike-based processing is a relatively
new approach that implements processing methods by manipulating spikes one by one at the time
they are transmitted, like a human brain. The mammal nervous system can solve much more complex
problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy
for visual information processing based on the neuro-inspired address-event-representation (AER)
is currently achieving very high performance. The aim of this work was to study the viability of a
matching mechanism in stereo-vision systems, using AER codification and its implementation in
a field-programmable gate array (FPGA). Some studies have been done before in an AER system
with monitored data using a computer; however, this kind of mechanism has not been implemented
directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied
and implemented, with other restrictions, in order to achieve good results in a real-time scenario.
The results and conclusions are shown, and the viability of its implementation is proven.Ministerio de Economía y Competitividad TEC2016-77785-
A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker
The study and monitoring of the behavior of wildlife has always been
a subject of great interest. Although many systems can track animal positions
using GPS systems, the behavior classification is not a common task. For this
work, a multi-sensory wearable device has been designed and implemented to be
used in the Doñana National Park in order to control and monitor wild and semiwild
life animals. The data obtained with these sensors is processed using a
Spiking Neural Network (SNN), with Address-Event-Representation (AER)
coding, and it is classified between some fixed activity behaviors. This works
presents the full infrastructure deployed in Doñana to collect the data, the wearable
device, the SNN implementation in SpiNNaker and the classification
results.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
Initially, robots were developed with the aim of making our life easier, carrying
out repetitive or dangerous tasks for humans. Although they were able
to perform these tasks, the latest generation of robots are being designed
to take a step further, by performing more complex tasks that have been
carried out by smart animals or humans up to date. To this end, inspiration
needs to be taken from biological examples. For instance, insects are able
to optimally solve complex environment navigation problems, and many researchers
have started to mimic how these insects behave. Recent interest in
neuromorphic engineering has motivated us to present a real-time, neuromorphic,
spike-based Central Pattern Generator of application in neurorobotics,
using an arthropod-like robot. A Spiking Neural Network was designed and
implemented on SpiNNaker. The network models a complex, online-change
capable Central Pattern Generator which generates three gaits for a hexapod
robot locomotion. Recon gurable hardware was used to manage both
the motors of the robot and the real-time communication interface with the
Spiking Neural Networks. Real-time measurements con rm the simulation
results, and locomotion tests show that NeuroPod can perform the gaits
without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-
Sound Recognition System Using Spiking and MLP Neural Networks
In this paper, we explore the capabilities of a sound classification
system that combines a Neuromorphic Auditory System for feature extraction
and an artificial neural network for classification. Two models of neural network
have been used: Multilayer Perceptron Neural Network and Spiking Neural
Network. To compare their accuracies, both networks have been developed and
trained to recognize pure tones in presence of white noise. The spiking neural
network has been implemented in a FPGA device. 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. Both systems are able to distinguish
the different sounds even in the presence of white noise. The recognition system
based in a spiking neural networks has better accuracy, above 91 %, even when
the sound has white noise with the same power.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA
Neural networks algorithms are commonly used to
recognize patterns from different data sources such as audio or
vision. In image recognition, Convolutional Neural Networks are
one of the most effective techniques due to the high accuracy they
achieve. This kind of algorithms require billions of addition and
multiplication operations over all pixels of an image. However,
it is possible to reduce the number of operations using other
computer vision techniques rather than frame-based ones, e.g.
neuromorphic frame-free techniques. There exists many neuromorphic
vision sensors that detect pixels that have changed
their luminosity. In this study, an event-based convolution engine
for FPGA is presented. This engine models an array of leaky
integrate and fire neurons. It is able to apply different kernel
sizes, from 1x1 to 7x7, which are computed row by row, with a
maximum number of 64 different convolution kernels. The design
presented is able to process 64 feature maps of 7x7 with a latency
of 8.98 s.Ministerio de Economía y Competitividad TEC2016-77785-
Spiking row-by-row FPGA Multi-kernel and Multi-layer Convolution Processor.
Spiking convolutional neural networks have become
a novel approach for machine vision tasks, due to the latency
to process an input stimulus from a scene, and the low power
consumption of these kind of solutions. Event-based systems only
perform sum operations instead of sum of products of framebased
systems. In this work an upgrade of a neuromorphic
event-based convolution accelerator for SCNN, which is able to
perform multiple layers with different kernel sizes, is presented.
The system has a latency per layer from 1.44 μs to 9.98μs for
kernel sizes from 1x1 to 7x7
Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets
Although it is not a novel topic, pattern recognition has
become very popular and relevant in the last years. Different classification
systems like neural networks, support vector machines or even
complex statistical methods have been used for this purpose. Several
works have used these systems to classify animal behavior, mainly in an
offline way. Their main problem is usually the data pre-processing step,
because the better input data are, the higher may be the accuracy of the
classification system. In previous papers by the authors an embedded
implementation of a neural network was deployed on a portable device
that was placed on animals. This approach allows the classification to
be done online and in real time. This is one of the aims of the research
project MINERVA, which is focused on monitoring wildlife in Do˜nana
National Park using low power devices. Many difficulties were faced when
pre-processing methods quality needed to be evaluated. In this work, a
novel pre-processing evaluation system based on self-organizing maps
(SOM) to measure the quality of the neural network training dataset is
presented. The paper is focused on a three different horse gaits classification
study. Preliminary results show that a better SOM output map
matches with the embedded ANN classification hit improvement.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-
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