Ensemble Wrapper Subsampling for Deep Modulation Classification

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems. Unlike traditional approaches that rely on pre-designed strategies that are solely based on expert knowledge, the proposed data-driven subsampling strategy employs deep neural network architectures to simulate the effect of removing candidate combinations of samples from each training input vector, in a manner inspired by how wrapper feature selection models work. The subsampled data is then processed by another deep learning classifier that recognizes each of the considered 10 modulation types. We show that the proposed subsampling strategy not only introduces drastic reduction in the classifier training time, but can also improve the classification accuracy to higher levels than those reached before for the considered dataset. An important feature herein is exploiting the transferability property of deep neural networks to avoid retraining the wrapper models and obtain superior performance through an ensemble of wrappers over that possible through solely relying on any of them.Comment: 22 pages, 13 figures, 2 table

Gamma synchronization of the hippocampal spatial map---topological model

The mammalian hippocampus plays a principal role in producing a cognitive map of space---an internalized representation of the animal's environment. The neuronal mechanisms producing this map depend primarily on the temporal structure of the hippocampal neurons' spiking activity, which is modulated by the oscillatory extracellular electrical field potential. In this paper, we discuss the integrative effect of the gamma rhythm, one of the principal components of these oscillations, on the ability of the place cell ensembles to encode a spatial map. Using methods of algebraic topology and statistical physics, we demonstrate that gamma-modulation of neuronal activity generates a synchronized spiking of dynamical cell assemblies, which enables learning a spatial map at faster timescales.Comment: 14 pages, 4 figures, 7 supplementary figure

Audio segmentation based on melodic style with hand-crafted features and with convolutional neural networks

We investigate methods for the automatic labeling of the taan section, a prominent structural component of the Hindustani Khayal vocal concert. The taan contains improvised raga-based melody rendered in the highly distinctive style of rapid pitch and energy modulations of the voice. We propose computational features that capture these specific high-level characteristics of the singing voice in the polyphonic context. The extracted local features are used to achieve classification at the frame level via a trained multilayer perceptron (MLP) network, followed by grouping and segmentation based on novelty detection. We report high accuracies with reference to musician annotated taan sections across artists and concerts. We also compare the performance obtained by the compact specialized features with frame-level classification via a convolutional neural network (CNN) operating directly on audio spectrogram patches for the same task. While the relatively simple architecture we experiment with does not quite attain the classification accuracy of the hand-crafted features, it provides for a performance well above chance with interesting insights about the ability of the network to learn discriminative features effectively from labeled data.Comment: This work was done in 2015 at Indian Institute of Technology, Bombay, as a part of the ERC grant agreement 267583 (CompMusic) projec

Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The Gibbs sampling method converges to the optimal Bayesian solution and, using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assume that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis and on prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog

Using context to make gas classifiers robust to sensor drift

The interaction of a gas particle with a metal-oxide based gas sensor changes the sensor irreversibly. The compounded changes, referred to as sensor drift, are unstable, but adaptive algorithms can sustain the accuracy of odor sensor systems. This paper shows how such a system can be defined without additional data acquisition by transfering knowledge from one time window to a subsequent one after drift has occurred. A context-based neural network model is used to form a latent representation of sensor state, thus making it possible to generalize across a sequence of states. When tested on samples from unseen subsequent time windows, the approach performed better than drift-naive and ensemble methods on a gas sensor array drift dataset. By reducing the effect that sensor drift has on classification accuracy, context-based models may be used to extend the effective lifetime of gas identification systems in practical settings

Over the Air Deep Learning Based Radio Signal Classification

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequency offset, symbol rate, and multi-path fading in simulation and conduct over-the-air measurement of radio classification performance in the lab using software radios and compare performance and training strategies for both. Finally we conclude with a discussion of remaining problems, and design considerations for using such techniques.Comment: 13 pages, 22 figure

Classification of multiple electromagnetic interference events in high-voltage power plant

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the expert’s knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events

Auditory information loss in real-world listening environments

Whether animal or speech communication, environmental sounds, or music -- all sounds carry some information. Sound sources are embedded in acoustic environments that contain any number of additional sources that emit sounds that reach the listener's ears concurrently. It is up to the listener to decode the acoustic informational mix, determine which sources are of interest, decide whether extra resources should be allocated to extracting more information from them, or act upon them. While decision making is a high-level process that is accomplished by the listener's cognition, selection and elimination of acoustic information is manifest along the entire auditory system, from periphery to cortex. This review examines latent informational paradigms in hearing research and demonstrates how several hearing mechanisms conspire to gradually eliminate information from the auditory sensory channel. It is motivated through the computational need of the brain to decomplexify unpredictable real-world signals in real time. Decomplexification through information loss is suggested to constitute a unifying principle of the mammalian hearing system, which is specifically demonstrated in human hearing. This perspective can be readily generalised to other sensory modalities.Comment: 17 pages, 2 figure

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

Cross-Country Skiing Gears Classification using Deep Learning

Human Activity Recognition has witnessed a significant progress in the last decade. Although a great deal of work in this field goes in recognizing normal human activities, few studies focused on identifying motion in sports. Recognizing human movements in different sports has high impact on understanding the different styles of humans in the play and on improving their performance. As deep learning models proved to have good results in many classification problems, this paper will utilize deep learning to classify cross-country skiing movements, known as gears, collected using a 3D accelerometer. It will also provide a comparison between different deep learning models such as convolutional and recurrent neural networks versus standard multi-layer perceptron. Results show that deep learning is more effective and has the highest classification accuracy.Comment: 15 pages, 8 figures, 1 tabl