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Urban acoustic classification (UAC) plays a vital role in smart city engineering, urban security, noise pollution analysis, etc. Convolutional neural networks (CNNs) based feature transfer learning have been shown competitive performance in many applications but little attention has been paid to UAC. In this study, a novel UAC algorithm exploiting the deep CNNs based feature transfer learning and the deep belief net (DBN) based classification is developed. The spectrogram is first adopted for the urban acoustic stream representation. Then, three deep CNNs pre-trained on ImageNet database are applied as feature extractors. The extracted features are concatenated and fed to a DBN for classifier learning. To achieve a good generalization performance, three restricted boltzmann machines (RBM) trained by the contrastive divergence algorithm (CD) followed by a back-propagation (BP) based fine parameter tuning is adopted in DBN. The proposed UAC is evaluated on a real acoustic database, including 11 categories of acoustic signals recorded from the urban environment. Performance comparisons to many state-of-the-art algorithms are presented to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers. Inspired by kernel learning, a kernel version of ML-ELM is developed, namely, multilayer kernel ELM (ML-KELM), whose contributions are: 1) elimination of manual tuning on the number of hidden nodes in every layer; 2) no random projection mechanism so as to obtain optimal model generalization; 3) exact inverse solution for output weights is guaranteed under invertible kernel matrix, resulting to smaller reconstruction error; and 4) all transformation matrices are unified into two matrices only, so that storage can be reduced and may shorten model execution time. Benchmark data sets of different sizes have been employed for the evaluation of ML-KELM. Experimental results have verified the contributions of the proposed ML-KELM. The improvement in accuracy over benchmark data sets is up to 7%. [ABSTRACT FROM PUBLISHER]

Deformation of gait silhouettes caused by different view angles heavily affects the performance of gait recognition. In this paper, a new method based on deterministic learning and knowledge fusion is proposed to eliminate the effect of view angle for efficient view-invariant gait recognition. First, the binarized walking silhouettes are characterized with three kinds of time-varying width parameters. The nonlinear dynamics underlying different individuals' width parameters is effectively approximated by radial basis function (RBF) neural networks through deterministic learning algorithm. The extracted gait dynamics captures the spatio-temporal characteristics of human walking, represents the dynamics of gait motion, and is shown to be insensitive to the variance across various view angles. The learned knowledge of gait dynamics is stored in constant RBF networks and used as the gait pattern. Second, in order to handle the problem of view change no matter the variation is small or large, the learned knowledge of gait dynamics from different views is fused by constructing a deep convolutional and recurrent neural network (CRNN) model for later human identification task. This knowledge fusion strategy can take advantage of the encoded local characteristics extracted from the CNN and the long-term dependencies captured by the RNN. Experimental results show that promising recognition accuracy can be achieved. [ABSTRACT FROM AUTHOR]