325 research outputs found

    Sound Recognition System Using Spiking and MLP Neural Networks

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    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

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

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    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

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

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    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-
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