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- W3100597003 abstract "Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN." @default.
- W3100597003 created "2020-11-23" @default.
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- W3100597003 date "2017-01-01" @default.
- W3100597003 modified "2023-10-17" @default.
- W3100597003 title "Bio-inspired Multi-layer Spiking Neural Network Extracts Discriminative Features from Speech Signals" @default.
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- W3100597003 doi "https://doi.org/10.1007/978-3-319-70136-3_95" @default.
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