Nonlinear Hebbian Learning for noise-independent vehicle sound recognition

In this paper we propose using a new approach, a nonlinear Hebbian learning, to implement acoustic signature recognition of running vehicles. The proposed learning rule processes both time and frequency components of input data. The spectral analysis is realized using auditory gammatone filterbanks. The gammatone-filtered feature vectors are then assembled over multiple temporal frames to establish a high-dimensional spectro-temporal representation (STR). With the exact acoustic signature of running vehicles being unknown, a nonlinear Hebbian learning (NHL) rule is employed to extract representative independent features from the spectro-temporal ones and to reduce the dimensionality of the feature space. During learning, synaptic weights between input and output neurons are adaptively learned. Motivated by neurobiological synaptic transmission in the brain, one specific nonlinear activation function, which can represent multiple independent neural signaling pathways, is proposed to process nonlinear Hebbian learning. It is shown that this function satisfies the requirements of the activation function in nonlinear neural learning, and that its derivative matches the implicit distribution of vehicle sounds, thus leading to a statistically optimal learning. Simulation results show that both STR and NHL can accurately extract critical features from original input data. The proposed model achieves better performance under noisy environments than its counterparts. For additive white Gaussian noise and common colored noise, the proposed model demonstrates excellent robustness. It can decrease the error rate to 3% with improvement 21 ~ 34% at signal-to-noise ratio (SNR)= 0 dB, and can function efficiently with error rate 7 ~ 8% at low SNR=-6 dB when its counterparts cannot work properly at this situation. To summarize, this study not only provides an efficient way to capture important features from high-dimensional input signals but also offers robustness against severe background noise.