Optimal Short-Time Features for Music/Speech Classification of Compressed Audio Data

2006 ◽  
Author(s):  
A. Rizzi ◽  
M. Buccino ◽  
M. Panella ◽  
A. Uncini
Keyword(s):  
Audio Data ◽  
Speech Classification ◽  
Short Time

scholarly journals DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heni Sulistiani ◽  
Ahmad Ari Aldino
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Grant Program ◽  
C4.5 Algorithm ◽  
Long Time ◽  
The Right ◽  
Short Time ◽  
Tuition Fee

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system.

scholarly journals Luxatio lentis in dogs: a case report

2008 ◽  
Vol 52 (No. 5) ◽  
pp. 213-222
Author(s):  
M. Saroglu ◽  
O.D. Erdikmen ◽  
O. Guzel ◽  
D. Aydin
Keyword(s):  
Case Report ◽  
Small Animal ◽  
Intraocular Inflammation ◽  
The Other ◽  
Lens Extraction ◽  
Posterior Direction ◽  
Short Time ◽  
Anterior Direction ◽  
Lens Luxation

The material of the present study was composed of 30 eyes with luxatio lentis occurring in 20 dogs. Unilateral lens luxation was determined in a half of the animals while bilateral luxation was determined in the other half. Lenses in 19 of the 30 eyes were luxated in the anterior direction (63.3%), three were luxated in the posterior direction (10%) and eight were subluxated (26.6%). The distribution of patient dogs based on the breeds showed that the incidence of lens luxation was higher in Terrier, Cocker Spaniel and crossbreed dogs compared to the other breeds. The results of etiological classification of the diseases which are generally seen in older dogs (on average 7.2 years old) were as follows: congenital in two patients, primary luxation in four patients, and secondary luxation in 14 patients. Secondary luxations diagnosed in 14 animals were determined to have developed as a result of trauma in two dogs, glaucoma in one dog, uveitis in one dog, and senile degeneration and/or cataract in 10 dogs. Bilateral (two dogs) and unilateral (five dogs) intracapsullar lens extraction (ICLE) was applied to these animals. The primary disease was to be kept under control by treating those with secondary lens luxation for uveitis or glaucoma. A severe progressive intraocular inflammation developed in one patient. Enucleation was conducted on this patient due to buphthalmus developing in a short time. These results may be helpful to small animal veterinarians dealing with this disease which results in blindness unless early diagnosis and surgical treatment are conducted.

scholarly journals Moving Towards a Finer Way of Light-Cured Resin-Based Restorative Dental Materials: Recent Advances in Photoinitiating Systems Based on Iodonium Salts

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4093
Author(s):  
Monika Topa ◽  
Joanna Ortyl
Keyword(s):  
Biomedical Applications ◽  
Dental Materials ◽  
Dental Composites ◽  
Iodonium Salts ◽  
Photoinduced Polymerization ◽  
Low Energy Consumption ◽  
Restorative Dental Materials ◽  
Phosphine Derivatives ◽  
Short Time

The photoinduced polymerization of monomers is currently an essential tool in various industries. The photopolymerization process plays an increasingly important role in biomedical applications. It is especially used in the production of dental composites. It also exhibits unique properties, such as a short time of polymerization of composites (up to a few seconds), low energy consumption, and spatial resolution (polymerization only in irradiated areas). This paper describes a short overview of the history and classification of different typical monomers and photoinitiating systems such as bimolecular photoinitiator system containing camphorquinone and aromatic amine, 1-phenyl-1,2-propanedione, phosphine derivatives, germanium derivatives, hexaarylbiimidazole derivatives, silane-based derivatives and thioxanthone derivatives used in the production of dental composites with their limitations and disadvantages. Moreover, this article represents the challenges faced when using the latest inventions in the field of dental materials, with a particular focus on photoinitiating systems based on iodonium salts. The beneficial properties of dental composites cured using initiation systems based on iodonium salts have been demonstrated.

Genre classification of compressed audio data

2008 ◽  
Author(s):  
Antonello Rizzi ◽  
Nicola Maurizio Buccino ◽  
Massimo Panella ◽  
Aurelio Uncini
Keyword(s):  
Genre Classification ◽  
Audio Data

scholarly journals Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

2020 ◽  
Vol 10 (9) ◽  
pp. 3097
Author(s):  
Dmitry Kaplun ◽  
Alexander Voznesensky ◽  
Sergei Romanov ◽  
Valery Andreev ◽  
Denis Butusov
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Feature Space ◽  
Roc Curves ◽  
Harmonic Wavelets ◽  
Intelligence System ◽  
Stationary Signals ◽  
Harmonic Wavelet ◽  
Short Time

This paper considers two approaches to hydroacoustic signal classification, taking the sounds made by whales as an example: a method based on harmonic wavelets and a technique involving deep learning neural networks. The study deals with the classification of hydroacoustic signals using coefficients of the harmonic wavelet transform (fast computation), short-time Fourier transform (spectrogram) and Fourier transform using a kNN-algorithm. Classification quality metrics (precision, recall and accuracy) are given for different signal-to-noise ratios. ROC curves were also obtained. The use of the deep neural network for classification of whales’ sounds is considered. The effectiveness of using harmonic wavelets for the classification of complex non-stationary signals is proved. A technique to reduce the feature space dimension using a ‘modulo N reduction’ method is proposed. A classification of 26 individual whales from the Whale FM Project dataset is presented. It is shown that the deep-learning-based approach provides the best result for the Whale FM Project dataset both for whale types and individuals.

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

Helicopter Cockpit Audio Data Analysis to Infer Flight State Information

2020 ◽  
Vol 65 (3) ◽  
pp. 1-8
Author(s):  
Sanghyun Shin ◽  
Abhishek Vaidya ◽  
Inseok Hwang
Keyword(s):  
Transportation Safety ◽  
Sound Analysis ◽  
Engine Torque ◽  
Audio Analysis ◽  
Audio Data ◽  
Characteristic Features ◽  
Cumulative Sum Control ◽  
Using Data ◽  
Short Time ◽  
Occurrence Times

In recent years, the National Transportation Safety Board has highlighted the importance of analyzing flight data as one of the effective methods to improve the safety and efficiency of helicopter operations. Since cockpit audio data contain various sounds from engines, alarms, crew conversations, and other sources within a cockpit, analyzing cockpit audio data can help identify the causes of incidents and accidents. Among the various types of the sounds in cockpit audio data, this paper focuses on cockpit alarm and engine sounds as an object of analysis. This paper proposes cockpit audio analysis algorithms, which can detect types and occurrence times of alarm sounds for an abnormal flight and estimate engine-related flight parameters such as an engine torque. This is achieved by the following: for alarm sound analysis, finding the highest correlation with the short time Fourier transform, and the Cumulative Sum Control Chart (CUSUM) using a database of the characteristic features of the alarm; and for engine sound analysis, using data mining and statistical modeling techniques to identify specific frequencies associated with engine operations. The proposed algorithm is successfully applied to a set of simulated audio data, which were generated by the X-plane flight simulator, and real audio data, which were recorded by GoPro cameras in Sikorsky S-76 helicopters to demonstrate its desired performance.

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