Musical notes classification with neuromorphic auditory system using FPGA and a convolutional spiking network
| Autor: | Manuel Domínguez-Morales, Elena Cerezuela-Escudero, Alejandro Linares-Barranco, Gabriel Jimenez-Moreno, Angel Jimenez-Fernandez, R. Paz-Vicente |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación |
| Rok vydání: | 2015 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Address-event-representation Computer science Speech recognition White noise Set (abstract data type) Convolutional spiking network medicine.anatomical_structure Neuromorphic engineering Neuromorphic auditory hardware Computer Science::Sound Frequency domain medicine Auditory system Spike (software development) Musical note recognition Field-programmable gate array Cochlea |
| Zdroj: | IJCNN idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| DOI: | 10.1109/ijcnn.2015.7280619 |
| Popis: | 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-C04-02 |
| Databáze: | OpenAIRE |
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