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In this paper, we deal with the classification of Greek folk songs into 8 classes associated with the region of origin of the songs. Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality...
This paper presents an unsupervised approach to vocal detection in music recordings based on dictionary learning. At a first stage, the recording to be segmented is treated as training data and the K-SVD algorithm is used to learn a dictionary which sparsely represents a short-term feature sequence that has been extracted from the recording. Subsequently, the vectors of the feature sequence are reconstructed...
Automatic classification of human epithelial type-2 (HEp-2) cells can improve the diagnostic process of autoimmune diseases (ADs) in terms of lower cost, faster response, and better repeatability. However, most of the proposed methods for classification of HEp-2 cells suffer from several constraints including tedious parameter tuning, massive memory requirement, and high computational costs. We propose...
Genre classification can be considered as an essential part of music and movie recommender systems. So far, various automatic music genre classification methods have been proposed based on various audio features. However, such content-centric features are not capable of capturing the personal preferences of the listener. In this study, we provide preliminary experimental evidence for the possibility...
Music Structural Analysis (MSA) algorithms analyze songs with the purpose of automatically retrieving their large-scale structure. They do so from a feature-based representation of the audio signal (e.g., MFCCs, chromagram), which is usually hand-designed for that specific application. In order to design a proper audio representation for MSA, we need to assess which musical properties are relevant...
Detection of whispered speech in the presence of high levels of background noise has applications in fraudulent behaviour recognition. For instance, it can serve as an indicator of possible insider trading. We propose a deep neural network (DNN)-based whispering detection system, which operates on both magnitude and phase features, including the group delay feature from all-pole models (APGD). We...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
A new efficient and user-independent technique for the detection of muscle activation (MA) intervals is proposed based on Gaussian Mixture Model (GMM) and Ant Colony Classifier (AntCC). First, time and frequency features are extracted from the surface electromyography (sEMG) signals. Then, GMM is used to cluster these extracted features into burst & non burst. Those features with their class name...
Obssesive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of the patients' daily lives. Accurate diagnosis of this disorder is a primary step towards effective treatment. Diagnosing OCD is a lengthy procedure that involves interviews, symptom rating scales and behavioral observation as well as the experience of a clinician. Discovering signal processing and network...
This paper reports on development of a bird call recognition application and signal processing coding framework referred to as the “Bird Call Heuristic-based Identification and Recognition Program” (B-CHIRP for short). The aim of this project was development of an application that serves two purposes: provides a system to assist novice birders in the identification of birds based on their sounds,...
Extensive consumption of cereals as food in different domestic cousins places great demand the detection of cereal pest and struggle against them. Sunn pests such as Eurygaster integriceps, Eurygaster austriaca, Aelia rostrata and Aelia acuminata are insects with similar seasonal behaviors and dominant threat to the cereal plantations of Turkey. In this work, a microphone which works in acoustic and...
This paper proposes a new approach for detection and classification of power quality (PQ) disturbances in time domain. Most research in this field employs frequency domain analysis tools to analyse the features of PQ disturbances, such as Fourier transform and wavelet transform. However, the transient and steady-state characteristics of PQ disturbances are originally reflected on the waveforms of...
Currently, in the field of road safety, research is moving towards the use of electronic driving support systems that are capable of simulating human perception. These systems have more and more facilities, flexibilities and human development, to respond effectively against the delicate situations in the real world, which require the development of more efficient, fast, accurate signal processing...
Herein presents a novel neural decision forest by bootstrap aggregating randomized neural decision trees with the purpose of untangling signal data representations and performing heirarchical classification. The individual trees in a neural decision forest embed randomized neural networks (rNet) with 1-hidden layer acting as bottlenecks and hard gating functions. The rNets are trained using a multi-class...
Electrical activity in the heart is given by electrocardiogram (ECG) signal. Manual analysis of ECG beat is very time consuming task as it may contain hundreds of thousands of beats for 24 hours of ECG signal. This study gives a robust classification model for ECG using Rough Set Theory (RST). RST generates rules which are simple and more apprehensible for the user causing the extraction of more accurate...
Compressed Sensing (CS) allows to efficiently acquire and compress a signal within a single operation. However, reconstructing the original signal is typically expensive. Hence, being able to perform signal processing operations in the compressed domain is extremely important. In this paper we propose a new technique to perform classification tasks in the compressed domain. In order to perform compressive...
With the development of the Internet, authentication and copyright protection of digital products becomes an important issue. In this paper, a zero-watermarking algorithm based on audio content is proposed, which can stand against synchronization attacks. The audio signal is divided into several segments according to the size of watermarking image. After that, SVD transform is performed for each matrix...
Electromagnetic radiation signals from computer displays can be a computer security risk if the radiation signal is intercepted and reconstructed. In addition to the information displayed by the computer, electromagnetic radiation leaks the information of the computer itself which is more important for some attackers, protectors and security inspection workers. In this paper, we demonstrate that electromagnetic...
Electronic nose is a system, which can determine the fingerprint of gas sample by a sensor array coupled to pattern recognition system. In this paper, a sensor array based on WO3 gas sensor has been described, and a feature extraction technique, including integral and primary derivative, is reported, which leads to higher classification performance as compared to the classical features: fractional...
Robotic devices can be a viable solution in different rehabilitation activities for increasing patients' gains, providing high-frequent, repetitive and interactive rehabilitation treatments. In this paper, the design, development and preliminary characterization of a robotic system for assisted hand rehabilitation, driven by surface EMG measurements, based on the mirroring of healthy hand movements...
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