Print Email Facebook Twitter Sound-recognition using Spiking Neural Networks Title Sound-recognition using Spiking Neural Networks Author Prozée, Randy (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Signal Processing Systems) Contributor van Leuken, T.G.R.M. (mentor) Kumar, S.S. (graduation committee) Zjajo, Amir (graduation committee) Al-Ars, Z. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2021-02-26 Abstract The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-chip learning. This work implements and presents a viable architecture and training methodology to detect and classify audio data using Spiking Neural Networks. The architecture consists of two core components: the first component is an auditory front-end that performs low-level feature extraction. The second component is the SNN classifier supported by the spike encoder and decoder. The results show that the encoder has a major impact on the overall performance of the network. The temporal-based network is trained with help of common training methods, both supervised and unsupervised. The performance of the network is validated under both clean and different levels of noisy conditions. The impact on classification performance is analyzed and compared with traditional non-spiking Artificial Neural Networks. This in terms of classification accuracy, estimate energy consumption, and latency of inference. The proposed architectures achieve a max accuracy of 97.0% under ideal conditions. This is comparable to other non-spiking artificial neural networks, which require significantly more energy for inference. The implementation demonstrates that the architecture is a viable solution for detecting and classifying audio data. Subject Soud-recognitionspiking neural networksclassificationlow-power To reference this document use: http://resolver.tudelft.nl/uuid:52051ab7-c319-4a41-868c-1dd996cd7892 Embargo date 2023-02-26 Part of collection Student theses Document type master thesis Rights © 2021 Randy Prozée Files PDF 20210226_Randy_Prozee_462 ... tworks.pdf 2.18 MB Close viewer /islandora/object/uuid:52051ab7-c319-4a41-868c-1dd996cd7892/datastream/OBJ/view