Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1144

Search results for: music classification and tagging

1144 Machine Learning for Music Aesthetic Annotation Using MIDI Format: A Harmony-Based Classification Approach

Authors: Lin Yang, Zhian Mi, Jiacheng Xiao, Rong Li

Abstract:

Swimming with the tide of deep learning, the field of music information retrieval (MIR) experiences parallel development and a sheer variety of feature-learning models has been applied to music classification and tagging tasks. Among those learning techniques, the deep convolutional neural networks (CNNs) have been widespreadly used with better performance than the traditional approach especially in music genre classification and prediction. However, regarding the music recommendation, there is a large semantic gap between the corresponding audio genres and the various aspects of a song that influence user preference. In our study, aiming to bridge the gap, we strive to construct an automatic music aesthetic annotation model with MIDI format for better comparison and measurement of the similarity between music pieces in the way of harmonic analysis. We use the matrix of qualification converted from MIDI files as input to train two different classifiers, support vector machine (SVM) and Decision Tree (DT). Experimental results in performance of a tag prediction task have shown that both learning algorithms are capable of extracting high-level properties in an end-to end manner from music information. The proposed model is helpful to learn the audience taste and then the resulting recommendations are likely to appeal to a niche consumer.

Keywords: Harmonic analysis, machine learning, music classification and tagging, MIDI.

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1143 A method for Music Classification Based On Perceived Mood Detection for Indian Bollywood Music

Authors: Vallabha Hampiholi

Abstract:

A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context

Keywords: Mood, music classification, music genre, rhythm, music analysis.

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1142 FCA-based Conceptual Knowledge Discovery in Folksonomy

Authors: Yu-Kyung Kang, Suk-Hyung Hwang, Kyoung-Mo Yang

Abstract:

The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.

Keywords: Folksonomy data mining, formal concept analysis, collaborative tagging, conceptual knowledge discovery, classification.

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1141 Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF)

Authors: Noor A. Draman, Campbell Wilson, Sea Ling

Abstract:

Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.

Keywords: Bio-inspired audio content-based retrieval framework, features selection technique, low/high level features, artificial immune system

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1140 Spatial Audio Player Using Musical Genre Classification

Authors: Jun-Yong Lee, Hyoung-Gook Kim

Abstract:

In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.

Keywords: Automatic equalization, genre classification, music segment detection, spatial audio processing.

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1139 Part of Speech Tagging Using Statistical Approach for Nepali Text

Authors: Archit Yajnik

Abstract:

Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: Hidden Markov model, Viterbi algorithm, POS tagging, natural language processing.

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1138 Target Signal Detection Using MUSIC Spectrum in Noise Environment

Authors: Sangjun Park, Sangbae Jeong, Moonsung Han, Minsoo hahn

Abstract:

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. The algorithm detects the DOAs of multiple sources using the inverse of the eigenvalue-weighted eigen spectra. To apply the algorithm to target signal detection for GSC-based beamforming, we utilize its spectral response for the target DOA in noisy conditions. For evaluation of the algorithm, the performance of the proposed target signal detection method is compared with that of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics(ROC) curves.

Keywords: Beamforming, direction of arrival, multiple signal classification, target signal detection.

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1137 Automatic Musical Genre Classification Using Divergence and Average Information Measures

Authors: Hassan Ezzaidi, Jean Rouat

Abstract:

Recently many research has been conducted to retrieve pertinent parameters and adequate models for automatic music genre classification. In this paper, two measures based upon information theory concepts are investigated for mapping the features space to decision space. A Gaussian Mixture Model (GMM) is used as a baseline and reference system. Various strategies are proposed for training and testing sessions with matched or mismatched conditions, long training and long testing, long training and short testing. For all experiments, the file sections used for testing are never been used during training. With matched conditions all examined measures yield the best and similar scores (almost 100%). With mismatched conditions, the proposed measures yield better scores than the GMM baseline system, especially for the short testing case. It is also observed that the average discrimination information measure is most appropriate for music category classifications and on the other hand the divergence measure is more suitable for music subcategory classifications.

Keywords: Audio feature, information measures, music genre.

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1136 Normalized Cumulative Spectral Distribution in Music

Authors: Young-Hwan Song, Hyung-Jun Kwon, Myung-Jin Bae

Abstract:

As the remedy used music becomes active and meditation effect through the music is verified, people take a growing interest about psychological balance or remedy given by music. From traditional studies, it is verified that the music of which spectral envelop varies approximately as 1/f (f is frequency) down to a frequency of low frequency bandwidth gives psychological balance. In this paper, we researched signal properties of music which gives psychological balance. In order to find this, we derived the property from voice. Music composed by voice shows large value in NCSD. We confirmed the degree of deference between music by curvature of normalized cumulative spectral distribution. In the music that gives psychological balance, the curvature shows high value, otherwise, the curvature shows low value.

Keywords: Cognitive Psychology, Normalized Cumulative Spectral Distribution, Curvature.

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1135 Performance Evaluation of Music and Minimum Norm Eigenvector Algorithms in Resolving Noisy Multiexponential Signals

Authors: Abdussamad U. Jibia, Momoh-Jimoh E. Salami

Abstract:

Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks.

Keywords: Eigenvector, minimum norm, multiexponential, subspace.

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1134 Music Aptitude and School Readiness in Indonesian Children

Authors: Diella Gracia Martauli

Abstract:

This study investigated the relationship between music aptitude and school readiness in Indonesian children. Music aptitude is described as children’s music potential, whereas school readiness is defined as a condition in which a child is deemed ready to enter the formal education system. This study presents a hypothesis that music aptitude is correlated with school readiness. This is a correlational research study of 17 children aged 5-6 years old (M = 6.10, SD = 0.33) who were enrolled in a kindergarten school in Jakarta, Indonesia. Music aptitude scores were obtained from Primary Measures of Music Audiation, whereas School readiness scores were obtained from Bracken School Readiness Assessment Third Edition. The analysis of the data was performed using Pearson Correlation. The result found no correlation between music aptitude and school readiness (r = 0.196, p = 0.452). Discussions regarding the results, perspective from the measures and cultures are presented. Further study is recommended to establish links between music aptitude and school readiness.

Keywords: Bracken school readiness assessment, music aptitude, primary measures of music audiation, school readiness.

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1133 Effective Digital Music Retrieval System through Content-based Features

Authors: Bokyung Sung, Kwanghyo Koo, Jungsoo Kim, Myung-Bum Jung, Jinman Kwon, Ilju Ko

Abstract:

In this paper, we propose effective system for digital music retrieval. We divided proposed system into Client and Server. Client part consists of pre-processing and Content-based feature extraction stages. In pre-processing stage, we minimized Time code Gap that is occurred among same music contents. As content-based feature, first-order differentiated MFCC were used. These presented approximately envelop of music feature sequences. Server part included Music Server and Music Matching stage. Extracted features from 1,000 digital music files were stored in Music Server. In Music Matching stage, we found retrieval result through similarity measure by DTW. In experiment, we used 450 queries. These were made by mixing different compression standards and sound qualities from 50 digital music files. Retrieval accurate indicated 97% and retrieval time was average 15ms in every single query. Out experiment proved that proposed system is effective in retrieve digital music and robust at various user environments of web.

Keywords: Music Retrieval, Content-based, Music Feature and Digital Music.

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1132 Musical Instrument Classification Using Embedded Hidden Markov Models

Authors: Ehsan Amid, Sina Rezaei Aghdam

Abstract:

In this paper, a novel method for recognition of musical instruments in a polyphonic music is presented by using an embedded hidden Markov model (EHMM). EHMM is a doubly embedded HMM structure where each state of the external HMM is an independent HMM. The classification is accomplished for two different internal HMM structures where GMMs are used as likelihood estimators for the internal HMMs. The results are compared to those achieved by an artificial neural network with two hidden layers. Appropriate classification accuracies were achieved both for solo instrument performance and instrument combinations which demonstrates that the new approach outperforms the similar classification methods by means of the dynamic of the signal.

Keywords: hidden Markov model (HMM), embedded hidden Markov models (EHMM), MFCC, musical instrument.

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1131 Using Music in the Classroom to Help Syrian Refugees Deal with Post-War Trauma

Authors: Vartan Agopian

Abstract:

Millions of Syrian families have been displaced since the beginning of the Syrian war, and the negative effects of post-war trauma have shown detrimental effects on the mental health of refugee children. While educational strategies have focused on vocational training and academic achievement, little has been done to include music in the school curriculum to help these children improve their mental health. The literature of music education and psychology, on the other hand, shows the positive effects of music on traumatized children, especially when it comes to dealing with stress. This paper presents a brief literature review of trauma, music therapy, and music in the classroom, after having introduced the Syrian war and refugee situation. Furthermore, the paper highlights the benefits of using music with traumatized children from the literature and offers strategies for teachers (such as singing, playing an instrument, songwriting, and others) to include music in their classrooms to help Syrian refugee children deal with post-war trauma.

Keywords: Children, music, refugees, Syria, war.

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1130 Orchestra Course Outcomes in Terms of Values Education

Authors: Z. Kurtaslan, H. Hakan Okay, E. Can Dönmez, I. Kuçukdoğan

Abstract:

Music education aims to bring up individuals most appropriately and to advanced levels as a balanced whole physically, cognitively, affectively, and kinesthetically while making a major contribution to the physical and spiritual development of the individual. The most crucial aim of music education, an influential education medium per se, is to make music be loved; yet, among its educational aims are concepts such as affinity, friendship, goodness, philanthropy, responsibility, and respect all extremely crucial bringing up individuals as a balanced whole. One of the most essential assets of the music education is the training of making music together, solidifying musical knowledge and enabling the acquisition of cooperation. This habit requires internalization of values like responsibility, patience, cooperativeness, respect, self-control, friendship, and fairness. If musicians lack these values, the ensemble will become after some certain time a cacophony. In this qualitative research, the attitudes of music teacher candidates in orchestra/chamber music classes will be examined in terms of values.

Keywords: Education, music, orchestra/chamber music, values.

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1129 Neuro-Fuzzy Based Model for Phrase Level Emotion Understanding

Authors: Vadivel Ayyasamy

Abstract:

The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.

Keywords: Emotions, sentences, phrases, classification, patterns, fuzzy, positive orientation, negative orientation.

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1128 Variations and Fugue on an Ancient Taiwanese Music: The Art of Combining Taiwanese Traditional Music and Western Composition in Kuo Chih-Yuan’s Piano Repertoire

Authors: Sheng Wei Hsu

Abstract:

Taiwanese composer Kuo Chih-Yuan (1921-2013) studied composition at Tokyo University of the Arts and was influenced by the musical nationalism prevailing in Japan at the time. Determined to create world-class contemporary works to represent Taiwan, he created music with elements of traditional Taiwanese music in ways that had not been done before. The aims of this study were to examine the traditional elements used in Kuo Chih-Yuan’s Variations and Fugue on an Ancient Taiwanese Music (1972), and how an understanding of these elements might guide pianists to interpret a more proper performance of his work was also presented in this study.

Keywords: Kuo Chih-Yuan, music analysis, piano works, Taiwanese traditional music.

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1127 Improved Automated Classification of Alcoholics and Non-alcoholics

Authors: Ramaswamy Palaniappan

Abstract:

In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics. Three modifications are introduced to improve the classification performance: i) increasing the gamma band spectral range by increasing the pass-band width of the used filter ii) the use of Multiple Signal Classification algorithm to obtain the power of the dominant frequency in gamma band VEP signals as features and iii) the use of the simple but effective knearest neighbour classifier. To validate that these two modifications do give improved performance, a 10-fold cross validation classification (CVC) scheme is used. Repeat experiments of the previously used methodology for the extended dataset are performed here and improvement from 94.49% to 98.71% in maximum averaged CVC accuracy is obtained using the modifications. This latest results show that VEP based classification of alcoholics is worth exploring further for system development.

Keywords: Alcoholic, Multilayer-perceptron, Nearest neighbour, Gamma band, MUSIC, Visual evoked potential.

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1126 Impact of Music on Brain Function during Mental Task using Electroencephalography

Authors: B. Geethanjali, K. Adalarasu, R. Rajsekaran

Abstract:

Music has a great effect on human body and mind; it can have a positive effect on hormone system. Objective of this study is to analysis the effect of music (carnatic, hard rock and jazz) on brain activity during mental work load using electroencephalography (EEG). Eight healthy subjects without special musical education participated in the study. EEG signals were acquired at frontal (Fz), parietal (Pz) and central (Cz) lobes of brain while listening to music at three experimental condition (rest, music without mental task and music with mental task). Spectral powers features were extracted at alpha, theta and beta brain rhythms. While listening to jazz music, the alpha and theta powers were significantly (p < 0.05) high for rest as compared to music with and without mental task in Cz. While listening to Carnatic music, the beta power was significantly (p < 0.05) high for with mental task as compared to rest and music without mental task at Cz and Fz location. This finding corroborates that attention based activities are enhanced while listening to jazz and carnatic as compare to Hard rock during mental task.

Keywords: Music, Brain Function, Electroencephalography (EEG), Mental Task, Features extraction parameters

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1125 Knowledge Based Chords Manipulation in MES

Authors: V. Hepsiba Mabel, K. Alagarsamy, Justus S.

Abstract:

Chord formation in western music notations is an intelligent art form which is learnt over the years by a musician to acquire it. Still it is a question of creativity that brings the perfect chord sequence that matches music score. This work focuses on the process of forming chords using a custom-designed knowledgebase (KB) of Music Expert System. An optimal Chord-Set for a given music score is arrived by using the chord-pool in the KB and the finding the chord match using Jusic Distance (JD). Conceptual Graph based knowledge representation model is followed for knowledge storage and retrieval in the knowledgebase.

Keywords: Knowledge, Music, Representation, Knowledgebase, Chord-Set.

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1124 Direction of Arrival Estimation Based on a Single Port Smart Antenna Using MUSIC Algorithm with Periodic Signals

Authors: Chen Sun, Nemai Chandra Karmakar

Abstract:

A novel direction-of-arrival (DOA) estimation technique, which uses a conventional multiple signal classification (MUSIC) algorithm with periodic signals, is applied to a single RF-port parasitic array antenna for direction finding. Simulation results show that the proposed method gives high resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only one RF port and one analogue-to-digital converter (ADC) are used in this antenna, which features low DC power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling between elements. Therefore, the technique has great potential to be implemented into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals, to provide additional position location (PL) services.

Keywords: Direction-of-arrival (DOA) estimation, electronically steerable parasitic array radiator (ESPAR), multiple single classifications (MUSIC), position location.

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1123 Classification Influence Index and its Application for k-Nearest Neighbor Classifier

Authors: Sejong Oh

Abstract:

Classification is an important topic in machine learning and bioinformatics. Many datasets have been introduced for classification tasks. A dataset contains multiple features, and the quality of features influences the classification accuracy of the dataset. The power of classification for each feature differs. In this study, we suggest the Classification Influence Index (CII) as an indicator of classification power for each feature. CII enables evaluation of the features in a dataset and improved classification accuracy by transformation of the dataset. By conducting experiments using CII and the k-nearest neighbor classifier to analyze real datasets, we confirmed that the proposed index provided meaningful improvement of the classification accuracy.

Keywords: accuracy, classification, dataset, data preprocessing

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1122 Application of a Novel Audio Compression Scheme in Automatic Music Recommendation, Digital Rights Management and Audio Fingerprinting

Authors: Anindya Roy, Goutam Saha

Abstract:

Rapid progress in audio compression technology has contributed to the explosive growth of music available in digital form today. In a reversal of ideas, this work makes use of a recently proposed efficient audio compression scheme to develop three important applications in the context of Music Information Retrieval (MIR) for the effective manipulation of large music databases, namely automatic music recommendation (AMR), digital rights management (DRM) and audio finger-printing for song identification. The performance of these three applications has been evaluated with respect to a database of songs collected from a diverse set of genres.

Keywords: Audio compression, Music Information Retrieval, Digital Rights Management, Audio Fingerprinting.

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1121 Effects of Computer–Based Instructional Designs among Pupils of Different Music Intelligence Levels

Authors: Aldalalah, M. Osamah, Soon Fook Fong

Abstract:

The purpose of this study was to investigate the effects of computer–based instructional designs, namely modality and redundancy principles on the attitude and learning of music theory among primary pupils of different Music Intelligence levels. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The independent variables were the three modes of courseware. The moderator variable was music intelligence. The dependent variables were the post test score. ANOVA was used to determine the significant differences of the pretest scores among the three groups. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. High music intelligence pupils performed significantly better than low music intelligence pupils in all the three treatment modes. The AI mode was found to help pupils with low music intelligence significantly more than the TI and AIT modes.

Keywords: Modality, Redundancy, Music theory, Cognitivetheory of multimedia learning, Cognitive load theory, Musicintelligence.

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1120 MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music

Authors: Brigitte Rafael, Stefan M. Oertl

Abstract:

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the internal structure of a composition. Structural information about a composition can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. The authors of this paper present the MTSSM framework, a twolayer framework for the multi-track segmentation of symbolic music. The strength of this framework lies in the combination of existing methods for local track segmentation and the application of global structure information spanning via multiple tracks. The first layer of the MTSSM uses various string matching techniques to detect the best candidate segmentations for each track of a multi-track composition independently. The second layer combines all single track results and determines the best segmentation for each track in respect to the global structure of the composition.

Keywords: Pattern Recognition, Music Information Retrieval, Machine Learning.

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1119 Touch Interaction through Tagging Context

Authors: Gabriel Chavira, Jorge Orozco, Salvador Nava, Eduardo Álvarez, Julio Rolón, Roberto Pichardo

Abstract:

Ambient Intelligence promotes a shift in computing which involves fitting-out the environments with devices to support context-aware applications. One of main objectives is the reduction to a minimum of the user’s interactive effort, the diversity and quantity of devices with which people are surrounded with, in existing environments; increase the level of difficulty to achieve this goal. The mobile phones and their amazing global penetration, makes it an excellent device for delivering new services to the user, without requiring a learning effort. The environment will have to be able to perceive all of the interaction techniques. In this paper, we present the PICTAC model (Perceiving touch Interaction through TAgging Context), which similarly delivers service to members of a research group.

Keywords: Ambient Intelligence, Tagging Context, Touch Interaction, Touching Services.

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1118 Developing of Thai Classical Music Ensemble in Rattanakosin Period

Authors: Pansak Vandee

Abstract:

The research titled “Developing of Thai Classical Music Ensemble in Rattanakosin Period" aimed 1) to study the history of Thai Classical Music Ensemble in Rattanakosin Period and 2) to analyze changing in each period of Rattanakosin Era. This is the historical and documentary research. The data was collected by in-depth interview those musicians, and academic music experts and field study. The focus group discussion was conducted to analyze and conclude the findings. The research found that the history of Thai Classical Music Ensemble in Rattanakosin Period derived from the Ayutthaya period. Thai classical music ensemble consisted of “Wong Pipat", “Wong Mahori", “Wong Kreang Sai". “Wong Kubmai", “Wong Krongkak", “Brass Band", and “Kan Band" which were used to ceremony, ritual, drama, performs and entertainment. Changed of the Thai music in the early Rattanakosin Period were passed from the Ayutthaya Period and the influence of the western civilization. New Band formed in Thai Music were “Orchestra" and “Contemporary Band". The role of Thai music was changed from the ceremonial rituals to entertainment. Development of the Thai music during the reign of King Rama 1 to King Rama 7, was improved from the court. But after the revolution, the musical patronage of the court was maintained by the Government. Thai Classical Music Ensemble were performed to be standard pattern.

Keywords: Development, Rattanakosin Period, Thai Classical Music Ensemble.

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1117 Interpreting Chopin’s Music Today: Mythologization of Art: Kitsch

Authors: Ilona Bala

Abstract:

The subject of this abstract is related to the notion of 'popular music', a notion that should be treated with extreme care, particularly when applied to Frederic Chopin, one of the greatest composers of Romanticism. By ‘popular music’, we mean a category of everyday music, set against the more intellectual kind, referred to as ‘classical’. We only need to look back to the culture of the nineteenth century to realize that this ‘popular music’ refers to the ‘music of the low’. It can be studied from a sociological viewpoint, or as sociological aesthetics. However, we cannot ignore the fact that, very quickly, this music spread to the wealthiest strata of the European society of the nineteenth century, while likewise the lowest classes often listen to the intellectual classical music, so pleasant to listen to. Further, we can observe that a sort of ‘sacralisation of kitsch’ occurs at the intersection between the classical and popular music. This process is the topic of this contribution. We will start by investigating the notion of kitsch through the study of Chopin’s popular compositions. However, before considering the popularisation of this music in today’s culture, we will have to focus on the use of the word kitsch in Chopin’s times, through his own musical aesthetics. Finally, the objective here will be to negate the theory that art is simply the intellectual definition of aesthetics. A kitsch can, obviously, only work on the emotivity of the masses, as it represents one of the features of culture-language (the words which the masses identify with). All art is transformed, becoming something outdated or even outmoded. Here, we are truly within a process of mythologization of art, through the study of the aesthetic reception of the musical work.

Keywords: F. Chopin, musical work, popular music, romantic music, mythologization of art, kitsch.

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1116 Review and Comparison of Associative Classification Data Mining Approaches

Authors: Suzan Wedyan

Abstract:

Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.

Keywords: Associative Classification, Classification, Data Mining, Learning, Rule Ranking, Rule Pruning, Prediction.

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1115 Musical Notation Reading versus Alphabet Reading - Comparison and Implications for Teaching Music Reading to Students with Dyslexia

Authors: Ora Geiger

Abstract:

This paper discusses the question whether a person diagnosed with dyslexia will necessarily have difficulty in reading musical notes. The author specifies the characteristics of alphabet reading in comparison to musical notation reading, and concludes that there should be no contra-indication for teaching standard music reading to children with dyslexia if an appropriate process is offered. This conclusion is based on a long term case study and relies on two main characteristics of music reading: (1) musical notation system is a systematic, logical, relative set of symbols written on a staff; and (2) music reading learning connected with playing a musical instrument is a multi-sensory activity that combines sight, hearing, touch, and movement. The paper describes music reading teaching procedures, using soprano recorders, and provides unique teaching methods that have been found to be effective for students who were diagnosed with dyslexia. It provides theoretical explanations in addition to guidelines for music education practices.

Keywords: Alphabet reading, music reading, multisensory teaching method, dyslexia, recorder playing.

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