Interfacing PDM sensors with PFM spiking systems: application for Neuromorphic Auditory Sensors

In this paper we present a sub-system to convert audio information from low-power MEMS microphones with pulse density modulation (PDM) output into rate coded spike streams. These spikes represent the input signal of a Neuromorphic Auditory Sensor (NAS), which is implemented with Spike Signal Processing (SSP) building blocks. For this conversion, we have designed a HDL component for FPGA able to interface with PDM microphones and converts their pulses to temporal distributed spikes following a pulse frequency modulation (PFM) scheme with an accurate configurable Inter-Spike-Interval. The new FPGA component has been tested in two scenarios, first as a stand-alone circuit for its characterization, and then it has been integrated with a full NAS design to verify its behavior. This PDM interface demands less than 1% of a Spartan 6 FPGA resources and has a power consumption below 5mW.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

Low Latency Event-Based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications

Dynamic Vision Sensor (DVS) pixels produce an asynchronous variable-rate address-event output that represents brightness changes at the pixel. Since these sensors produce frame-free output, they are ideal for real-time dynamic vision applications with real-time latency and power system constraints. Event-based ltering algorithms have been proposed to post-process the asynchronous event output to reduce sensor noise, extract low level features, and track objects, among others. These postprocessing algorithms help to increase the performance and accuracy of further processing for tasks such as classi cation using spike-based learning (ie. ConvNets), stereo vision, and visually-servoed robots, etc. This paper presents an FPGA-based library of these postprocessing event-based algorithms with implementation details; speci cally background activity (noise) ltering, pixel masking, object motion detection and object tracking. The latencies of these lters on the Field Programmable Gate Array (FPGA) platform are below 300ns with an average latency reduction of 188% (maximum of 570%) over the software versions running on a desktop PC CPU. This open-source event-based lter IP library for FPGA has been tested on two different platforms and scenarios using different synthesis and implementation tools for Lattice and Xilinx vendors

Development of adaptive transducer based on biological sensory mechanism

textAn adaptive sensor concept and prototype has been developed based on a sensing element which is analogous to and inspired by the arrangement of outer hair cells and inner hair cells between the basilar membrane and tectorial membrane which form the organ of corti in mammalian cochlea. The bio-inspired design was supported by development of a bond graph model of the electromotility (active response) of outer hair cells. Outer hair cells perform like actuators and simulation results using this model are compared with physiological data found in the literature to verify its characteristic response. Insight gained from the model is used to develop a sensor structure analogous to the organ of corti and designed to measure acceleration. A piezoelectric bimorph was selected as the transducer basis, and a bond graph model of the bimorph in an accelerometer configuration was formulated to aid control design and simulation. There is no published data regarding the type of information transmitted among the inner hair cells, outer hair cells, and brain. Consequently, a controller intended to adjust the adaptation process similar to what might exist in the cochlear system has been developed for the sensor and based on a model referenced adaptive control algorithm. Simulations verify that the algorithm can successfully control and enhance performance of the sensor. Practicability of the design is evaluated by a series of experiments on a prototype. This study focused on using a controller structure that was programmed, implemented, and tested using programmable logic based on FPGA technology. The experiments evaluated how well the adaptive sensor could meet a specified performance requirement. Implementation issues that arise, such as the need for differentiators in the adaptive controller or internal propagation of vibration within the sensor structure, hinder the tuning ability. Nevertheless, the trends indicate that the algorithm can meet the desired performance if certain limitations can be overcome. Finally, recommendations have been made for expansion of the research in such fields as an alternative structure for tuning, sensor networking, and reference sensor configuration.Mechanical Engineerin

Speech filtering for improving intelligibility in noisy transients

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.Hearing impairment is a problem that affects a large percentage of the population. Cochlear implants allow those with profound or total hearing loss to regain some hearing by stimulating auditory nerve fibers with implanted electrodes, in response to sound picked up by an external microphone. The signal processing chain from microphone input to stimulation output is an important factor in the overall speech intelligibility of the implant system. This thesis work improves on an existing ultra-low-power cochlear implant system by utilizing an improved noise and power efficient bandpass filter bank to implement a novel frequency-selective gain control algorithm capable of reducing, and in some cases removing, loud transient noises, thereby improving speech intelligibility. This gain control algorithm takes advantage of the inherent frequency-specific gain control afforded by the improved bandpass filter topology. This contribution makes an improvement to the existing state-of-the-art system in both power efficiency and performance.by Andrew Lewine.M.Eng