Norhalina Senan

Feature Extraction for Traditional Malay Musical Instruments Classification System

Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data pre-processing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation and feature extraction process. Thus, this study is conducted in order to investigate the impact of several factors that might affecting the classification accuracy such as audio length, segmented frame size and data sets size (for training and testing) towards Traditional Malay musical instruments sounds classification system. The perception-based and MFCC features schemes with total of 37 features was utilized in this study. Meanwhile, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets and extracted features schemes in terms of their classification performance. Results show that the highest accuracy of 99.57% was obtained from the best data sets with the combination of full features. It is also revealed that the identified factors had a significant role to the performance of classification accuracy. Hence, this study suggest that further feature analysis research is necessary for better solution in Traditional Malay musical instruments sounds classification system problem.

The ideal data representation for feature extraction of traditional Malay musical instrument sounds classification

In presenting the appropriate data sets, various data representation and feature extraction methods have been discovered previously. However, almost all the existing methods are utilized based on the Western musical instruments. In this study, the data representation and feature extraction methods are applied towards Traditional Malay musical instruments sounds classification. The impact of five factors that might affecting the classification accuracy which are the audio length, segmented frame size, starting point, data distribution and data fraction (for training and testing) are investigated. The perception-based and MFCC features schemes with total of 37 features was used. While, Multi-Layered Perceptrons classifier is employed to evaluate the modified data sets in terms of the classification performance. The results show that the highest accuracy of 97.37% was obtained from the best data sets with the combination of full features.

Rough Set Approach for Attributes Selection of Traditional Malay Musical Instruments Sounds Classification

Feature selection has become very important research in musical instruments sounds for handling the problem of ‘curse of dimensionality’. In literature, various feature selection techniques have been applied in this domain focusing on Western musical instruments sounds. However, study on feature selection using rough sets of non-Western musical instruments sounds including Malay Traditional musical instruments is inadequate and still needs an intensive research. Thus, in this paper, an alternative feature selection technique using rough set theory based on Maximum Degree of dependency of Attributes (MDA) technique proposed by [8] for Traditional Malay musical instruments sounds is proposed. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, feature selection using proposed technique and feature validation via classification. The results show that the highest classification accuracy of 99.82% was achieved from the best 17 features with 1-NN classifier.

Soft set theory for feature selection of traditional Malay musical instrument sounds

Computational models of the artificial intelligence such as soft set theory have several applications. Soft data reduction can be considered as a machine learning technique for features selection. In this paper, we present the applicability of soft set theory for feature selection of Traditional Malay musical instrument sounds. The modeling processes consist of three stages: feature extraction, data discretization and finally using the multi-soft sets approach for feature selection through dimensionality reduction in multivalued domain. The result shows that the obtained features of proposed model are 35 out of 37 attributes.

Application of Wavelet De-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set

Recent advances in the field of image processing have revealed that the level of noise in mammogram images highly affect the images quality and classification performance of the classifiers. Whilst, numerous data mining techniques have been developed to achieve high efficiency and effectiveness for computer aided diagnosis systems. However, fuzzy soft set theory has been merely experimented for medical images. Thus, this study proposed a classifier based on fuzzy soft set with embedding wavelet de-noising filters. Therefore, the proposed methodology involved five steps namely: MIAS dataset, wavelet de-noising filters hard and soft threshold, region of interest identification, feature extraction and classification. Therefore, the feasibility of fuzzy soft set for classification of mammograms images has been scrutinized. Experimental results show that proposed classifier FussCyier provides the classification performance with Daub3 (Level 1) with accuracy 75.64% (hard threshold), precision 46.11%, recall 84.67%, F-Micro 60%. Thus, the results provide an alternative technique to categorize mammogram images.

De-noising analysis of mammogram images in the wavelet domain using hard and soft thresholding

The noisy nature of digital mammograms and low contrast of suspicious areas which make medical images de-noising a challenging problem. Therefore, image de-noising is an important task in image processing, thus the use of wavelet transform provides better and improved quality of an image and reduces noise level. For medical images, many wavelets like db1, sym8, coif1, coif3 etc can be used for de-noising of a medical image. However, in this paper, haar, sym8 daubechies db3 (mallat), daubechies db4 at certain level of soft and hard threshold have been calculated. Later, peak signal to noise ratio (PSNR) values are calculated for these wavelet methods. These experiments help to select the best wavelet transform for the de-noising of particular medical images such as mammogram images.

Rough Set Theory for Feature Ranking of Traditional Malay Musical Instruments Sounds Dataset

This paper presents an alternative feature ranking technique for Traditional Malay musical instruments sounds dataset using rough-set theory based on the maximum degree of dependency of attributes. The modeling process comprises seven phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, and finally feature ranking using the proposed technique. The results show that the selected features generated from the proposed technique able to reduce the complexity process.

A Review on Violence Video Classification Using Convolutional Neural Networks

The volatile growth of social media content on the Internet is revolutionizing content distribution and social interaction. Social media exploded as a category of online discourse where people create content, share it, bookmark it and network it at prodigious rate. Examples comprise Facebook, MySpace, Youtube, Instagram, Digg, Twitter, Snapchat and others. Since it is easy to reach, use and the high velocity of spreading information among users. The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. In this paper, a review of applications of deep networks techniques has been presented. Hence, the existing literature suggests that we do not lose sight of the current and future potential of applications of deep network techniques. Thus, there is a high potential for the use of Convolutional Neural Networks (CNN) for violence video classification, which has not been fully investigated and would be one of the interesting directions for future research in video classification.

Effect of presence/absence of noise in mammogram images using fuzzy soft set based classification

Effective use of feature set and selection of a suitable classification method are significant for improving classification accuracy. However, mammogram images classification is affected by many factors such as additive gaussian noise, low contrast and artifacts. Therefore, the aim of this paper is to observe the impact of presence /absence of noise on the quality and classification accuracy of mammogram images. The proposed methodology involved five steps that are data collection, images de-noising using wavelet hard and soft thresholding, region of interest (ROI) identification, feature extraction (statistical texture features), and classification. Hundred and twelve images (68 benign images and 51 malignant images) were used for experimental set ups. The experimental results show the improvement of classification accuracy in the presence of noise using wavelet filter with fuzzy soft set classifier compared with the results in the absence of noise within existing classification algorithms.

A study on traditional Malay musical instruments sounds classification system

In recent year, most studies concerned with the classification of musical instruments sounds focus on western musical instruments. With the enormous amount of instruments data and features schemes, adapting the existing techniques for classifying the traditional Malay musical instruments sounds might not be as easy due to the differences in the sounds samples used. Thus, the existing framework and techniques that have been proposed for automatic musical instruments sounds classification system will be reviewed and evaluated especially on their performance in achieving the highest accuracy rate. As a result, a new framework for Traditional Malay Musical Instruments Sounds Classification System and the classification accuracy achieved in the preliminary experiment are presented.