@inproceedings{942abc916b9743d59ebc017c24c61484,
title = "A spiking neural network model for sound recognition",
abstract = "This paper presents a spiking neural network (SNN) model of leaky integrate-and-fire (LIF) neurons for sound recognition, which provides a way to simulate the brain processes. Neural coding and learning by processing external stimulus and recognizing different patterns are important parts in SNN model. Based on features extracted from the time-frequency representation of sound, we present a time-frequency encoding method which can retain the adequate information of original sound and generate spikes from represented features. The generated spikes are further used to train the SNN model with plausible supervised synaptic learning rule to efficiently perform various classification tasks. By testing the encoding and learning methods in RWCP database, experiments demonstrate that the proposed SNN model can achieve the robust performance for sound recognition across a variety of noise conditions.",
keywords = "Information, Sound recognition, Spiking neural network, Temporal coding, Temporal network, Time-frequency",
author = "Rong Xiao and Rui Yan and Huajin Tang and Tan, {Kay Chen}",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China under grant number 61673283. Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016 ; Conference date: 19-11-2016 Through 23-11-2016",
year = "2017",
doi = "10.1007/978-981-10-5230-9_57",
language = "English",
isbn = "9789811052293",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "584--594",
editor = "Fuchun Sun and Huaping Liu and Dewen Hu",
booktitle = "Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers",
}