Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
Abstract
:1. Introduction
2. SCTN-Spike Continuous Time Neuron Model
2.1. The Leaky Integrate and Fire (LIF) Neuron Model
2.2. STDP Learning Module
3. Building Block Approach
3.1. SCTN-Based Phase Shifting
3.2. SCTN-Based Phase Shifting
4. SCTN-Based Sound Feature Extraction
4.1. Classical Sound Preprocessing
4.2. SCTN-Based Resonators Applied to Features Extraction
5. Experimental and Results
6. Conclusions
7. Patents
Author Contributions
Funding
Conflicts of Interest
References
- Yepes, A.J.; Tang, J.; Mashford, B.S. Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips. arXiv 2017, arXiv:1705.07755. [Google Scholar]
- Tang, J.; Mashford, B.S.; Yepes, A.J. Semantic Labeling Using a Low-Power Neuromorphic Platform. IEEE Geosci. Remote Sens. Lett. 2018, 15, 1184–1188. [Google Scholar] [CrossRef]
- Cao, Y.; Chen, Y.; Khosla, D. Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vis. 2015, 113, 54–66. [Google Scholar] [CrossRef]
- Moradi, S.; Qiao, N.; Stefanini, F.; Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 2017, 12, 106–122. [Google Scholar] [CrossRef] [Green Version]
- Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef] [Green Version]
- Izhikevich, E.M. Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 2004, 15, 1063–1070. [Google Scholar] [CrossRef] [PubMed]
- Kasabov, N.K. Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2019. [Google Scholar]
- Doborjeh, Z.; Doborjeh, M.; Crook-Rumsey, M.; Taylor, T.; Wang, G.Y.; Moreau, D.; Krägeloh, C.; Wrapson, W.; Siegert, R.J.; Kasabov, N.; et al. Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture. Sensors 2020, 20, 7354. [Google Scholar] [CrossRef] [PubMed]
- Dytckov, S.; Daneshtalab, M. Computing with hardware neurons: Spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side. arXiv 2016, arXiv:1602.02009. [Google Scholar]
- Bensimon, M.; Greenberg, S.; Ben-Shimol, Y.; Haiut, M. A New SCTN Digital Low Power Spiking Neuron. IEEE Trans. Circuits Syst. II Exp. Briefs 2021. submitted for publication. [Google Scholar]
- Bensimon, M.; Greenberg, S.; Ben-Shimol, Y.; Haiut, M. A New Digital Low Power Spiking Neuron. Int. J. Future Comput. Commun. 2019, 8, 24–28. [Google Scholar] [CrossRef]
- Jawandhiya, P. Hardware design for machine learning. Int. J. Artif. Intell. Appl. 2018, 9, 63–84. [Google Scholar] [CrossRef]
- Jaiswal, A.; Roy, S.; Srinivasan, G.; Roy, K. Proposal for a leaky-integrate-fire spiking neuron based on magnetoelectric switching of ferromagnets. IEEE Trans. Electron. Devices 2017, 64, 1818–1824. [Google Scholar] [CrossRef]
- Schuman, C.D.; Potok, T.E.; Patton, R.M.; Birdwell, J.D.; Dean, M.E.; Rose, G.S.; Plank, J.S. A survey of neuromorphic computing and neural networks in hardware. arXiv 2017, arXiv:1705.06963. [Google Scholar]
- Cristini, A.; Salerno, M.; Susi, G. A continuous-time spiking neural network paradigm. In Advances in Neural Networks: Computational and Theoretical Issues; Springer: Berlin/Heidelberg, Germany, 2015; pp. 49–60. [Google Scholar]
- Wu, J.; Chua, Y.; Zhang, M.; Li, H.; Tan, K.C. A spiking neural network framework for robust sound classification. Front. Neurosci. 2018, 12, 836. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Greff, K.; Srivastava, R.K.; Koutník, J.; Steunebrink, B.R.; Schmidhuber, J. LSTM: A search space odyssey. IEEE Trans. Neural Networks Learn. Syst. 2016, 28, 2222–2232. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Diehl, P.U.; Zarrella, G.; Cassidy, A.; Pedroni, B.U.; Neftci, E. Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In Proceedings of the 2016 IEEE International Conference on Rebooting Computing (ICRC), San Diego, CA, USA, 17–19 October 2016; pp. 1–8. [Google Scholar]
- Song, S.; Miller, K.D.; Abbott, L.F. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 2000, 3, 919–926. [Google Scholar] [CrossRef]
- Brette, R. Computing with neural synchrony. PLoS Comput. Biol. 2012, 8, e1002561. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Masquelier, T. STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons. Neuroscience 2018, 389, 133–140. [Google Scholar] [CrossRef] [PubMed]
- Tirumala, S.S.; Shahamiri, S.R.; Garhwal, A.S.; Wang, R. Speaker identification features extraction methods: A systematic review. Expert Syst. Appl. 2017, 90, 250–271. [Google Scholar] [CrossRef]
- Prabakaran, D.; Shyamala, R. A Review on Performance of Voice Feature Extraction Techniques. In Proceedings of the 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 21–22 February 2019; pp. 221–231. [Google Scholar]
- Davis, S.; Mermelstein, P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Process. 1980, 28, 357–366. [Google Scholar] [CrossRef] [Green Version]
- Palaz, D.; Magimai-Doss, M.; Collobert, R. End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition. Speech Commun. 2019, 108, 15–32. [Google Scholar] [CrossRef] [Green Version]
- Xiao, R.; Yan, R.; Tang, H.; Tan, K.C. A spiking neural network model for sound recognition. In International Conference on Cognitive Systems and Signal Processing, Proceedings of the ICCSIP 2016: Cognitive Systems and Signal Processing, Beijing, China, 19–23 November 2016; Springer: Berlin/Heidelberg, Germany, 2016; pp. 584–594. [Google Scholar]
- Cassidy, A.S.; Merolla, P.; Arthur, J.V.; Esser, S.K.; Jackson, B.; Alvarez-Icaza, R.; Datta, P.; Sawada, J.; Wong, T.M.; Feldman, V.; et al. Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 4–9 August 2013; pp. 1–10. [Google Scholar]
- Bing, Z.; Meschede, C.; Huang, K.; Chen, G.; Rohrbein, F.; Akl, M.; Knoll, A. End to end learning of spiking neural network based on r-stdp for a lane keeping vehicle. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21–25 May 2018; pp. 1–8. [Google Scholar]
- Tang, G.; Shah, A.; Michmizos, K.P. Spiking neural network on neuromorphic hardware for energy-efficient unidimensional SLAM. arXiv 2019, arXiv:1903.02504. [Google Scholar]
- Li, X.; Wang, W.; Xue, F.; Song, Y. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity. Phys. A Stat. Mech. Its Appl. 2018, 491, 716–728. [Google Scholar] [CrossRef]
- Cassidy, A.; Andreou, A.G.; Georgiou, J. A combinational digital logic approach to STDP. In Proceedings of the 2011 IEEE international Symposium of Circuits and Systems (ISCAS), Rio de Janeiro, Brazil, 15–18 May 2011; pp. 673–676. [Google Scholar]
- Frenkel, C.; Lefebvre, M.; Legat, J.D.; Bol, D. A 0.086-mm 212.7-pJ/SOP 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS. IEEE Trans. Biomed. Circuits Syst. 2018, 13, 145–158. [Google Scholar]
- Diehl, P.U.; Cook, M. Efficient implementation of STDP rules on SpiNNaker neuromorphic hardware. In Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China, 6–11 July 2014; pp. 4288–4295. [Google Scholar]
- Yousefzadeh, A.; Stromatias, E.; Soto, M.; Serrano-Gotarredona, T.; Linares-Barranco, B. On practical issues for stochastic stdp hardware with 1-bit synaptic weights. Front. Neurosci. 2018, 12, 665. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gerstner, W.; Kistler, W.M.; Naud, R.; Paninski, L. Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- Bahoura, M.; Ezzaidi, H. Hardware implementation of MFCC feature extraction for respiratory sounds analysis. In Proceedings of the 2013 8th International Workshop on Systems, Signal Processing and Their Applications (WoSSPA), Algiers, Algeria, 12–15 May 2013; pp. 226–229. [Google Scholar]
- Nakamura, S.; Hiyane, K.; Asano, F.; Nishiura, T.; Yamada, T. Acoustical sound database in real environments for sound scene understanding and hands-free speech recognition. In Proceedings of the 2nd International Conference on Language Resources and Evaluation, Athens, Greece, 31 May–2 June 2000; pp. 965–968. [Google Scholar]
- Dennis, J.; Yu, Q.; Tang, H.; Tran, H.D.; Li, H. Temporal coding of local spectrogram features for robust sound recognition. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26–31 May 2013; pp. 803–807. [Google Scholar]
Model | Accuracy (%) |
---|---|
RNN | 95.35 |
LSTM | 98.40 |
LSF-SNN | 98.50 |
LTF-SNN | 97.50 |
SOM-SNN | 99.60 |
SCTN-SNN | 98.73 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Bensimon, M.; Greenberg, S.; Haiut, M. Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing. Sensors 2021, 21, 1065. https://doi.org/10.3390/s21041065
Bensimon M, Greenberg S, Haiut M. Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing. Sensors. 2021; 21(4):1065. https://doi.org/10.3390/s21041065
Chicago/Turabian StyleBensimon, Moshe, Shlomo Greenberg, and Moshe Haiut. 2021. "Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing" Sensors 21, no. 4: 1065. https://doi.org/10.3390/s21041065
APA StyleBensimon, M., Greenberg, S., & Haiut, M. (2021). Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing. Sensors, 21(4), 1065. https://doi.org/10.3390/s21041065