4 research outputs found
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
The capability for environmental sound recognition (ESR) can determine the
fitness of individuals in a way to avoid dangers or pursue opportunities when
critical sound events occur. It still remains mysterious about the fundamental
principles of biological systems that result in such a remarkable ability.
Additionally, the practical importance of ESR has attracted an increasing
amount of research attention, but the chaotic and non-stationary difficulties
continue to make it a challenging task. In this study, we propose a spike-based
framework from a more brain-like perspective for the ESR task. Our framework is
a unifying system with a consistent integration of three major functional parts
which are sparse encoding, efficient learning and robust readout. We first
introduce a simple sparse encoding where key-points are used for feature
representation, and demonstrate its generalization to both spike and non-spike
based systems. Then, we evaluate the learning properties of different learning
rules in details with our contributions being added for improvements. Our
results highlight the advantages of the multi-spike learning, providing a
selection reference for various spike-based developments. Finally, we combine
the multi-spike readout with the other parts to form a system for ESR.
Experimental results show that our framework performs the best as compared to
other baseline approaches. In addition, we show that our spike-based framework
has several advantageous characteristics including early decision making, small
dataset acquiring and ongoing dynamic processing. Our framework is the first
attempt to apply the multi-spike characteristic of nervous neurons to ESR. The
outstanding performance of our approach would potentially contribute to draw
more research efforts to push the boundaries of spike-based paradigm to a new
horizon.Comment: 13 pages,12 figure
A Bin Encoding Training of a Spiking Neural Network-based Voice Activity Detection
Advances of deep learning for Artificial Neural Networks(ANNs) have led to
significant improvements in the performance of digital signal processing
systems implemented on digital chips. Although recent progress in low-power
chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs)
based applications offer an even lower power consumption, as a consequence of
the ensuing sparse spike-based coding scheme. In this work, we develop a
SNN-based Voice Activity Detection (VAD) system that belongs to the building
blocks of any audio and speech processing system. We propose to use the bin
encoding, a novel method to convert log mel filterbank bins of single-time
frames into spike patterns. We integrate the proposed scheme in a bilayer
spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our
approach shows that SNNs enable an ultra low-power implementation of a VAD
classifier that consumes only 3.8W, while achieving state-of-the-art
performance.Comment: 5 pages, 3 figures, 1 tabl
Synaptic Learning with Augmented Spikes
Traditional neuron models use analog values for information representation
and computation, while all-or-nothing spikes are employed in the spiking ones.
With a more brain-like processing paradigm, spiking neurons are more promising
for improvements on efficiency and computational capability. They extend the
computation of traditional neurons with an additional dimension of time carried
by all-or-nothing spikes. Could one benefit from both the accuracy of analog
values and the time-processing capability of spikes? In this paper, we
introduce a concept of augmented spikes to carry complementary information with
spike coefficients in addition to spike latencies. New augmented spiking neuron
model and synaptic learning rules are proposed to process and learn patterns of
augmented spikes. We provide systematic insight into the properties and
characteristics of our methods, including classification of augmented spike
patterns, learning capacity, construction of causality, feature detection,
robustness and applicability to practical tasks such as acoustic and visual
pattern recognition. The remarkable results highlight the effectiveness and
potential merits of our methods. Importantly, our augmented approaches are
versatile and can be easily generalized to other spike-based systems,
contributing to a potential development for them including neuromorphic
computing.Comment: 13 page
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
Spikes are the currency in central nervous systems for information
transmission and processing. They are also believed to play an essential role
in low-power consumption of the biological systems, whose efficiency attracts
increasing attentions to the field of neuromorphic computing. However,
efficient processing and learning of discrete spikes still remains as a
challenging problem. In this paper, we make our contributions towards this
direction. A simplified spiking neuron model is firstly introduced with effects
of both synaptic input and firing output on membrane potential being modeled
with an impulse function. An event-driven scheme is then presented to further
improve the processing efficiency. Based on the neuron model, we propose two
new multi-spike learning rules which demonstrate better performance over other
baselines on various tasks including association, classification, feature
detection. In addition to efficiency, our learning rules demonstrate a high
robustness against strong noise of different types. They can also be
generalized to different spike coding schemes for the classification task, and
notably single neuron is capable of solving multi-category classifications with
our learning rules. In the feature detection task, we re-examine the ability of
unsupervised STDP with its limitations being presented, and find a new
phenomenon of losing selectivity. In contrast, our proposed learning rules can
reliably solve the task over a wide range of conditions without specific
constraints being applied. Moreover, our rules can not only detect features but
also discriminate them. The improved performance of our methods would
contribute to neuromorphic computing as a preferable choice.Comment: 13 page