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The role of HG in the analysis of temporal iteration and interaural correlation
Automatic Workflow Monitoring in Industrial Environments
Robust automatic workflow monitoring using visual sensors in industrial environments is still an unsolved problem. This is mainly due to the difficulties of recording data in work settings and the environmental conditions (large occlusions, similar background/foreground) which do not allow object detection/tracking algorithms to perform robustly. Hence approaches analysing trajectories are limited in such environments. However, workflow monitoring is especially needed due to quality and safety requirements. In this paper we propose a robust approach for workflow classification in industrial environments. The proposed approach consists of a robust scene descriptor and an efficient time series analysis method. Experimental results on a challenging car manufacturing dataset showed that the proposed scene descriptor is able to detect both human and machinery related motion robustly and the used time series analysis method can classify tasks in a given workflow automatically
Design of a Neuromemristive Echo State Network Architecture
Echo state neural networks (ESNs) provide an efficient classification technique for spatiotemporal signals. The feedback connections in the ESN enable feature extraction in both spatial and temporal components in time series data. This property has been used in several application domains such as image and video analysis, anomaly detection, and speech recognition. The software implementations of the ESN demonstrated efficiency in processing such applications, and have low design cost and flexibility. However, hardware implementation is necessary for power constrained resources applications such as therapeutic and mobile devices. Moreover, software realization consumes an order or more power compared to the hardware realization. In this work, a hardware ESN architecture with neuromemristive system is proposed. A neuromemristive system is a brain inspired computing system that uses memristive devises for synaptic plasticity. The memristive devices in neuromemristive systems have several interesting properties such as small footprint, simple device structure, and most importantly zero static power dissipation. The proposed architecture is reconfigurable for different ESN topologies. 2-D mesh architecture and toroidal networks are exploited in the reservoir layer. The relation between performance of the proposed reservoir architecture and reservoir metrics are analyzed. The proposed architecture is tested on a suite of medical and human computer interaction applications. The benchmark suite includes epileptic seizure detection, speech emotion recognition, and electromyography (EMG) based finger motion recognition. The proposed ESN architecture demonstrated an accuracy of , , and for epileptic seizure detection, speech emotion recognition and EMG prosthetic fingers control respectively
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Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations
Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices.
In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations.
We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues
Over the last few years, a rapidly increasing number of Internet-of-Things
(IoT) systems that adopt voice as the primary user input have emerged. These
systems have been shown to be vulnerable to various types of voice spoofing
attacks. Existing defense techniques can usually only protect from a specific
type of attack or require an additional authentication step that involves
another device. Such defense strategies are either not strong enough or lower
the usability of the system. Based on the fact that legitimate voice commands
should only come from humans rather than a playback device, we propose a novel
defense strategy that is able to detect the sound source of a voice command
based on its acoustic features. The proposed defense strategy does not require
any information other than the voice command itself and can protect a system
from multiple types of spoofing attacks. Our proof-of-concept experiments
verify the feasibility and effectiveness of this defense strategy.Comment: Proceedings of the 27th International Conference on Computer
Communications and Networks (ICCCN), Hangzhou, China, July-August 2018. arXiv
admin note: text overlap with arXiv:1803.0915
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