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Feature extraction plays a very important role in the speech classification process because a better feature is good for improving the classification rate. This paper presents a speech feature extraction method by using Discrete Wavelet Transform (DWT) at 7th level of decomposition with mother wavelet of Dau-bechies 2, Renyi Entropy (RE), Autoregressive Power Spectral Density (AR-PSD), Statistical,...
Speech recognition systems are ubiquitous and find its application in automated voice control, voice dialling and automated directory assistance. This paper aims at implementing a neural network based isolated spoken word recognition system on an embedded board — Raspberry Pi using open source software called octave. Mel-Frequency Cepstral Coefficient (MFCC) features are extracted from speech signal...
Nowadays' road network infrastructure failing to cope up with the exponential increase in vehicular population, there is a constant strive to find smarter ways to deal with it using existing infrastructure. Intelligent Transport System is at the forefront of this, one of the aims is accurate and sophisticated traffic predictions that ensure smooth and hassle free commuting and administrative experience...
Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While...
Many operators are working in jobs that require stressful mental tasks such as transportation supervision, vehicle driving, banking and others. Prevention of fatigued-based human error, that has been a standing challenge in such work areas, can be detected and quantified using human performance level. This paper proposes an enhanced method for operator fatigue detection based on computer-keyboard...
While the majority of exploratory approaches search for correlations among features of different modalities, indirect/nonlinear relations between structure and function have not yet been fully investigated. In this work, we employ a neural machine translation model [1] to relate two modalities: structural MRI (sMRI) spatial components and functional MRI (fMRI) brain states estimated using a dynamic...
Neural Network (NN) based acoustic frontends, such as denoising autoencoders, are actively being investigated to improve the robustness of NN based acoustic models to various noise conditions. In recent work the joint training of such frontends with backend NNs has been shown to significantly improve speech recognition performance. In this paper, we propose an effective algorithm to jointly train...
Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images...
We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages...
This work present new parameters based on biometrie handwritten information for the writer identification. The feature extraction is developed by new algorithms based on image processing techniques. The handwritten parameters will be classified by artificial neural network and fusion strategy in order to increase the accuracy. After experiments, and using a dataset composed by 100 writers, this proposal...
Sorting is an important step in processing and packing lines of pomegranate fruits. Currently pomegranates are sorted into quality categories manually. But manual sorting poses problems such as tediousness, low accuracy, subjectivity etc. Moreover, manual sorting is not recommended for export quality fruits. Hence a machine vision system is required in order to sort the pomegranate fruits. The present...
The main aim of recognising gestures is to build a system that can identify human gestures that are specific and then to use them to put forth desired information to the device. By using mathematical algorithms, human gestures can be interpreted. This is referred to as Gesture Recognition. Mudra is an expressive form of gesture that is mainly used in Indian classical dance form where the gesture is...
Coughing is one of the important signs of several diseases in dogs. There are two types of dog cough: dry cough and productive cough. The latter is most often associated with an infectious condition. It is difficult to differentiate between the two types even by experienced practitioners. In this paper, an automatic cough sound classification using neural network is introduced. A discrete wavelet...
This paper introduces to diagnosis of Dyslexia using computing system, considered people difficulties in reading, spelling, writing and speaking. Consequently, a computational analysis classifier will be achieved using dyslexia metrics techniques. Accordingly, Gibson test of brain skills will be used with effect of working memory, auditing (hearing and speech) and visual memory and cognition, visual...
As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains...
Traffic safety is an important problem for autonomous vehicles. The development of Traffic Sign Recognition (TSR) dedicated to reducing the number of fatalities and the severity of road accidents is an important and an active research area. Recently, most TSR approaches of machine learning and image processing have achieved advanced performance in traditional natural scenes. However, there exists...
The objective of this work is to convert printed text or handwritten characters recorded offline using either scanning equipment or cameras into a machine-usable text by simulating a neural network so that it would improve the process of collecting and storing data by human workers. Another goal is to provide an alternate, better and faster algorithm with higher accuracy to recognize the characters...
The popularity of smart devices and the increase of access rate are switching Internet from link-centric (host-to-host) to content-centric (user-to-content). Since the primary purpose of users is to find their desired contents from the Internet, the traditional host-to-host structure will cause lots of useless traffic and decrease the network efficiency. Content-centric network (CCN) is then introduced...
The main objective of this paper is prominent feature extraction from the EEG Signal using different wavelets and select the best suitable wavelet which gives excellent result and with the help of these features the EEG signal is classified into normal and abnormal category using Neural Networks. The EEG signal data is collected from the database available online (https://www.physionet.org/physiobank/database/)...
In this work, an offline signature identification system based on Histogram of Oriented Gradients (HOG) vector features is designed. Handwritten signature images are collected at Yildiz Technical University, from 15 people, 40 samples from each. Before the HOG feature extraction, size fixing and noise reduction processes are applied to all signature images. HOG features are extracted from the noiseless...
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