• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1997-2003
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• 2009

Methods for effective musical score recognition and efficient editing of musical scores are demanded because functions of computers for researches on musical activities become more and more important parts in recent days. In the conventional methods for handling musical scores manually, there are weak points such as incorrect score symbols in input process and requirement of much time to adjust the incorrect symbols. And also there is another weak point that the scores edited by each application program can be remodified by a specific application program only. In this paper, we proposed a method for automatic musical score recognition of printed musical scores in order to make up for the weak points. In the proposed method, staffs in a scanned score image are eliminated by horizontal histogram, noises are removed by 4 directional edge tracking algorithm, and then musical score symbols are extracted by using Grassfire algorithm. The extracted symbols are recognized by hierarchical ART2 algorithm. In order to evaluate the performance of the proposed method, we used 100 musical scores for experiment. In the experiment, we verified that the proposed method using hierarchical ART2 algorithm is efficient.

• Food Science and Biotechnology
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• v.14 no.6
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• pp.738-742
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• 2005

We developed a musical composition and performing method for the dynamic expression of food taste, to replace the static expression obtained from a numerical sensory score and linguistic method. The musical score was composed using cognitive food sensory data gathered while eating potato chips via a computer equipped with a sound card and a master keyboard, as well as Cakewalk 9.0, Goldwave 5.0, and Windows Media Player. The music score included four lines, one representing sensory data and three reflecting three layers of human consciousness. Different time units were applied by incorporating different repetition cycles for the relevant consciousness layers. The musical scores of individual subjects differed in terms of the score pattern and its sound. The music representing food taste was played by four instruments and showed different sound and acoustic frequency patterns for each individual subject during the consumption of potato chips. The musical expression method was applied to analyze the individual taste characteristics in the cognition of food.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1064-1069
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• 2013

In this paper, we propose a Musical Score Recognition with SOM and Enhanced ART-1 Algorithm. First, we apply Hough transform and Otsu's binarization to the original BMP format image and extract notes from separated passages by histogram analysis with removing staff lines. Then extracted musical notes are normalized to same size by SOM algorithm and ART-1 algorithm plays the learning and recognition role. The experiment verifies that the proposed method is quite effective on printed musical score recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1640-1645
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• 2010

There exists an inconvenience that players of each instrument pass musical score in orchestra recital. In this paper, we implement the electronic musical score transmitting system that possible to hand sheet music automatically, using Mini - ITX and LCD Panel. We made them unite with a sheet music pair, and it was made and a message and data transmission were embodied using reliable TCP/IP socket communication. In this paper, the client hands sheet music automatic, and server implemented the manual function to manage each clients individually. Because one band can be managed by one Access file all together, it is easy to manage each band. Along with this, this system selects TCP/IP as the reliable transfer protocol that protect loss and errors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.113-116
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• 2001

This paper describes an analysis of the music as a time series and the fuzzy logic-based modeling of it. All music is made up of a finite number of musical notations known as the musical symbols, such as clefs, staff, tine signature, notes, rests, etc. . The musical score uses musical symbols to present various characteristics, such as rhythm, melody, chord, etc,. for interpreting the music. In this paper, it is possible to transform the beat and pitch in the musical into time series from the viewpoint of recognizing beat and pitch of sounding tone at each time. On the basis of the identified features of the musical score, a musical score is represented as a time series and then is constructed to fuzzy logic-based model for predicting them. Examples are presented to illustrate the validity of the proposed method.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.838-843
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• 2021

This paper presents an algorithm for effective retrieval of score information to an input score image. The originality of the proposed algorithm is that it is designed to be robust to recognition errors by an OMR (Optical Music Recognition), while existing methods such as pitch histogram requires error induced OMR result be corrected before retrieval process. This approach helps people to retrieve score without training on music score for error correction. OMR takes a score image as input, recognizes musical symbols, and produces structural symbolic notation of the score as output, for example, in MusicXML format. Among the musical symbols on a score, it is observed that filled noteheads are rarely detected with errors with its simple black filled round shape for OMR processing. Barlines that separate measures also strong to OMR errors with its long uniform length vertical line characteristic. The proposed algorithm consists of a descriptor for a score and a similarity measure between a query score and a reference score. The descriptor is based on note-count, the number of filled noteheads in a measure. Each part of a score is represented by a sequence of note-count numbers. The descriptor is an n-gram sequence of the note-count sequence. Simulation results show that the proposed algorithm works successfully to a certain degree in score image-based retrieval for an erroneous OMR output.

• Journal of the Korean Home Economics Association
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• v.37 no.7
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• pp.83-94
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• 1999

The purpose of this study was to explore musical development of 3- to S-year-old children and their musical home environment. The subjects were one hundred ninety-four children and their mothers enrolled in four kindergartens in Seoul. Each child sang the birthday song with peers in a birthday play setting. It was audiotaped for the children to sing the song. Questionnaire of musical home environment developed by the researchers was used for the mothers. The children's rhythm and pitch development were coded by the scoring categories of Project Spectrum(Krechevsky, 1994). The data were analyzed by t-test, ANOVA, Scheffe, and Pearson correlation. The results of this study were as follows: Firstly, there was no a significant difference in the children's rhythm development among three age-groups as well as between boys and girls. Among rhythm subcategories, the unit of note was ranked in the highest score and the pulse the next. Secondly, there were significant differences in children's pitch development among three age-groups and between boys and girls. The older children significantly achieved higher scores than the younger. Among pitch subcategories, the contour was ranked in the highest score and the interval the next. Thirdly, the children's musical development and their physical home environment related to music were correlated positively. The children's pitch development was significantly related to the mothers' musical attitude and the children's rhythm development to the mothers' educational levels.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.13-23
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• 2024

Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.427-430
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• 1992

In this paper, we proposed an algorithm for the musical note recognition. Firstly, a given bit-mapped music score image is converted to a set of individual note pattern images via vertical projection. Then, the pitch of a note is determinal by comparison in the note-head position with the reference five-lines. Also, the length of a note is found via leader clustering with a set of normalized note patterns. Finally, a datafile to play the music is obtained using the pitch and length of musical notes. Experimental results with a simple musical score image show that the proposed scheme is performed well.

• Journal of Music and Human Behavior
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• v.6 no.2
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• pp.43-70
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• 2009

The purpose of this study was to find out how a vocal improvisation program can improve performance anxiety of students who are majoring in music. Four students participated in the vocal improvisation program for 8 weeks, and Spielberger's State-Trait Anxiety Inventory was administered prior to and after the completion of program. The following are results from this study. First, participants' anxiety score as measured by STAI-S significantly decreased 16.5 (p < .068) average. Second, a decrease in the subjects' practical anxiety level was quantitatively shown following this program the participants' anxiety score decreased on average by 25.5 (p < .068). Third, the subjects reported a positive musical experience as an outcome of this study. The results of the research as described above support priority researches that the music therapy program influences to decrease musical performance anxiety. The vocal improvisation program used in this study proved effective for decreasing general anxiety and releasing musical performance anxiety. This study indicates future uses of music therapy and music psychotherapy for college students who experience musical performance anxiety.