Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders

We show that a Modular Neural Network (MNN) can combine various speech enhancement modules, each of which is a Deep Neural Network (DNN) specialized on a particular enhancement job. Differently from an ordinary ensemble technique that averages variations in models, the propose MNN selects the best module for the unseen test signal to produce a greedy ensemble. We see this as Collaborative Deep Learning (CDL), because it can reuse various already-trained DNN models without any further refining. In the proposed MNN selecting the best module during run time is challenging. To this end, we employ a speech AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as similar as possible if its input is clean speech. Therefore, the AE can gauge the quality of the module-specific denoised result by seeing its AE reconstruction error, e.g. low error means that the module output is similar to clean speech. We propose an MNN structure with various modules that are specialized on dealing with a specific noise type, gender, and input Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always works better than an arbitrarily chosen DNN module and sometimes as good as an oracle result

Mean Field Bayes Backpropagation: scalable training of multilayer neural networks with binary weights

Significant success has been reported recently using deep neural networks for classification. Such large networks can be computationally intensive, even after training is over. Implementing these trained networks in hardware chips with a limited precision of synaptic weights may improve their speed and energy efficiency by several orders of magnitude, thus enabling their integration into small and low-power electronic devices. With this motivation, we develop a computationally efficient learning algorithm for multilayer neural networks with binary weights, assuming all the hidden neurons have a fan-out of one. This algorithm, derived within a Bayesian probabilistic online setting, is shown to work well for both synthetic and real-world problems, performing comparably to algorithms with real-valued weights, while retaining computational tractability

Neural self-tuning adaptive control of non-minimum phase system

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response

Minimizing End-to-End Delay and Maximizing Reliability using Multilayer Neural Network-based Hamming Back Propagation for Efficient Communication in WSN

Wireless sensor network (WSN) comprises number of spatially distributed sensor nodes for monitoring the physical environment conditions and arranging the gathered data at central location. WSN gained large attention in medical field, industry, military, etc. However, congestion control mechanism for communication between sensor nodes failed to consider the end-to-end delay features. In addition, it failed to handle reliability and not achieved the data concurrency. In order to address the above mentioned problems, Multilayer Neural Network-based Hamming Back Propagation (MNN-HBP) technique is introduced for efficient communication in WSN. In MNN-HBP technique, Amorphous View Point Algorithm is introduced for sensor node initialization for efficient communication in WSN. Amorphous View Point Algorithm used time of arrival to measure the time distance between the sender node and receiver node. After that Hamming Back Propagation Algorithm is used to identify the current location of the sensor nodes for minimizing the end-to end delay and improving the reliability. Each sensor node compares their distance with the neighbouring sensor nodes distance to identify the associated error. When the distance is higher, the associated error is higher and propagates error back to other sensor nodes in the previous layers. The process gets repeated until the communication established between source sensor and lower associated error nodes. By this way, efficient communication is carried out with higher reliability and minimum end-to end delay. Extensive simulation are conducted to illustrate the efficiency of proposed technique as well as the impacts of network parameters on end-to-end delay, reliability and data packets successful rate with respect to data packet size and number of data packets

Unmasking Optical Chaotic Cryptosystems Based on Delayed Optoelectronic Feedback

29 páginas, 22 figuras, 3 tablas.The authors analyze the security of optical chaotic communication systems. The chaotic carrier is generated by a laser diode subject to delayed optoelectronic feedback. Transmitters with one and two fixed delay times are considered. A new type of neural networks, modular neural networks, is used to reconstruct the nonlinear dynamics of the transmitter from experimental time series in the single-delay case, and from numerical simulations in single and two-delay cases. The authors show that the complexity of the model does not increase when the delay time is increased, in spite of the very high dimension of the chaotic attractor. However, it is found that nonlinear dynamics reconstruction is more difficult when the feedback strength is increased. The extracted model is used as an unauthorized receiver to recover the message. Therefore, the authors conclude that optical chaotic cryptosystems based on optoelectronic feedback systems with several fixed time delays are vulnerable.This work was supported by CICYT (Spain) under Project TEC2009-14581-C02-02.Peer reviewe