• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.857-860
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• 2017

현재 초보적인 능력을 가진 악기 연주자가 접근할 수 있는 하드웨어, 소프트웨어를 사용해 악기 연주법을 연습할 수 있는 수단은 전무하다. 따라서 본 논문은 악기 연주자가 연습을 하기 위해 사용할 수 있는 음 인식과 악보 정보의 처리, LSTM을 통한 자동 악보 생성의 복합적 기능을 가진 악기 보조 시스템을 제안한다. 또한 본 시스템은 기존의 FFT와 같은 일반적인 Pitch Detection 알고리즘보다 더 우월한 음 인식 성능을 보유한 Autocorrelation 전처리를 거친 LeNet-5 Convolutional Neural Network 모델을 사용하여 음 인식 성능을 높이는 기법을 제안한다. 이 음 인식 모델은 실험 결과 기존의 음 인식 기법보다 최대 약 5.4%의 성능 증가를 이루어냈다.

• Journal of Music and Human Behavior
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• v.18 no.2
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• pp.87-107
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• 2021

The purpose of this research was to explore the phenomenon of object relations with musical instruments as experienced by professional Samulnori musicians. The researcher conducted in-depth individual interviews with five Samulnori players who also completed questionnaires with open-ended questions. The data were analyzed using Giorgi(2004)'s phenomenological methodology. The results offered 121 semantic units, seven subcategories, and three main categories. The three main categories were transitional object, object of expression and recognition of internal desires, and object for recognition of others and communication. These results suggest that the ensemble format of Samulnori promotes the development of the musician's object relationship and can externalize the player's internalized representational system and interaction method. This study is significant in that it reveals the endopsychic functional relationship between a musician and their instrument and provides the basis for the use of Samulnori instruments in music therapy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.171-172
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• 2009

Nowadays the music market is getting bigger while students are losing their creativity because of the cramming education even in the music class. Based on the growing music market, we made a project for students to play the musical instruments more easily. The suggested program is different from any other system because, with this program, we can play the musical instruments if only we have a keyboard made of paper and Webcam, which made us save money. When an user put his finger on the paper keyboard, Webcam makes a sound recognizing the position of the finger on the keyboard. We can choose one instrument out of four; Piano, drum, base and guitar. With this system, we can get an opportunity to learn many sounds of musical instruments and make our own melody.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.77-79
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• 2014

본 논문에서는 Arduino의 I2C 통신을 통해 인터랙티브한 악기를 제안한다. 각각의 큐브는 서로를 인식하고 인식 후 집단이 된 큐브들은 하나의 악기가 된다. Arduino의 통신 핀을 알루미늄 호일에 연결하여 다른 큐브와 접촉 시 통신이 될 수 있도록 하였다. 통신이 이룬 큐브들을 rgbLED 색상 변화를 통해 시각적으로 접촉/비접촉을 나타냈다. 또한 Processing을 통해 그룹이 된 큐브들의 악기 소리를 제어하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.166-168
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• 2008

본 논문에서는 가상 악기 시스템의 제작에 대하여 기술한다. 가상 악기 시스템은 (이하 VMS라 칭한다.) 가상의 악기를 연주하는 것을 컴퓨터를 이용하여 체험해 볼 수 있게 한다. 또한 멀티 터치가 가능한 지능형 인터페이스 플랫폼인 Touch-Face를 기반으로 동시의 여러 사용자가 악기를 연주 할 수 있도록 한다. Touch-Face에서의 터치를 인식하여 맨손의 손동작에 의해 상호작용을 구현한다. 이 시스템은 특히 별도의 디바이스나 콘솔을 필요로 하지 않고 시스템 내에 가이드창이 있어 사용자로 하여금 쉽게 따라 할 수 있게 하고, 어린이들을 대상으로 하여 실감나는 연주 체험을 할 수 있으며, 악기를 다루는데 보다 경제적이고 효율적으로 학습하는데 활용될 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.305-312
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• 2005

Three classification algorithms are tested using musical instruments. Several classification algorithms are introduced and among them, Bayes rule, NN and k-NN performances evaluated. ZCR, mean, variance and average peak level feature vectors are extracted from instruments sample file and used as data set to classification system. Used musical instruments are Violin, baroque violin and baroque cello. Results of experiment show that the performance of NN algorithm excels other algorithms in musical instruments classification.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.107-114
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• 2010

A guitar, a piano, and a violin are popular musical instruments for User Created Contents(UCC). However the patterns of audio signal generated by a guitar and a piano are too similar to differentiate. The difference between two musical instruments can be found by analyzing the frequency variation per each band near signal peaks. The distribution of probability on the existence of signal peaks based on Cumulative Histogram were applied to musical instrument recognition. Experiments with statistical models of the frequency variation per each band near signal peaks showed the 14% improvement of musical instrument recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.377-379
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• 2021

According to a 2006 paper by a music therapist and a music therapist in elementary schools, the demand for special education increased, and a 2018 music education study showed that music rooms and equipment increased, but it was difficult to move or lacked various instruments. In this work, we develop a module that combines hardware and software for social improvement education in autistic children using tools. Various instrument sounds can be set using piezo sensors and Arduino, so you can experience various instruments through simple operation and there are instruments designed for music therapy through modularity. Hopefully, the study will help disabled children heal their music.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.743-744
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• 2023

타브(TAB) 악보는 주로 현악기에서 쓰이는 악보로, 일반적으로 생각하는 오선보 대신 악기의 줄 수만큼 선을 긋고 그 선 위에 프렛의 위치를 숫자 또는 문자로 표기한 형식의 악보이다. 본 논문에서는 입력된 PDF 형식의 타브 악보에서 OpenCV 를 사용하여 음표 및 악상 기호를 인식하는 시스템을 제안한다. 이 시스템은 사용자가 인식을 원하는 PDF 형식의 악보를 입력하면 PDF 파일을 PNG 파일로 변경한 뒤, 이를 OpenCV 를 활용하여 음표의 길이, 프렛의 위치 등 연주에 필요한 요소들만 객체 검출한 뒤 Tesseract 로 인식한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.148-155
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• 2014

In this paper a commute time embedding is implemented by organizing patches according to the graph-based metric, and its properties are investigated via changing the number of nodes on the graph.. It is shown that manifold embedding methods generate the intrinsic geometric structures when waveforms such as speech or music instrumental sound signals are embedded on the low dimensional Euclidean space. Basically manifold embedding algorithms only project the training samples on the graph into an embedding subspace but can not generalize the learning results to test samples. They are very effective for data clustering but are not appropriate for classification or recognition. In this paper a commute time guided transform is adopted to enhance the generalization ability and its performance is analyzed by applying it to the classification of 6 kinds of music instrumental sounds.