• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.381-384
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• 2013

본 논문에서는 허밍기반 음원 검색 시스템을 위한 오디오 특징 시퀀스 데이터 색인 기법을 제안한다. 우선 Query-by-Singing/Humming (QbSH) 시스템의 특징 데이터베이스를 생성하기 위하여 MP3 와 같은 다성음원에서 주요 멜로디를 추출하여 시퀀스데이터를 생성하고, 고속 검색을 지원하기 위한 시퀀스데이터를 색인화한다. 본 논문에서는 최소 Dynamic Time Warping (DTW) 거리 기법, 시퀀스 추상화 기법, 상한 값 기반 DTW 기법과 같이 세 가지의 시퀀스 데이터의 색인화 기술을 제시하고 각각에 대한 문제점을 파악하고, 성능을 평가한다. 이를 통하여 향상된 검색 시간과 검색 정확도를 얻을 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.257-259
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• 2012

이 논문에서는 실험을 통해서, 기존에 제안하였던 쿼리-바이-싱잉/허밍 (Query-by-singing/humming, QbSH) 시스템의 매칭 엔진의 강인성을 검증하고 그 결과를 제시한다. QbSH 시스템은 디지털 음악의 사용이 보편화되면서 음악 검색의 방법으로 많은 연구가 진행되어 오고 있다. QbSH 시스템은 입력으로부터 멜로디의 특징을 추출하는 부분과 추출된 특징을 매칭하는 부분으로 나눌 수 있는데, 매칭 단계에서 특징이 추출된 두 개의 멜로디 사이의 유사도 또는 거리를 계산하여 가장 유사한 멜로디를 데이터베이스에서 찾게 된다. 이 논문에서는 이 중, 기존에 제안하였던 매칭 엔진 부분의 강인성을 알아보기 위해서 입력으로부터 멜로디의 피치 시퀀스를 추출하는 과정을 간략히 하여 그 결과를 살펴보았다. 즉, 기존에 제안한 매칭 엔진이 특정한 피치 시퀀스 추출 과정에서만 좋은 성능을 보이는 게 아님을 실험을 통해서 살펴보았다. 실험 결과, 피치 시퀀스를 추출하는 과정이 극도로 간략해지더라도, 매칭 엔진을 좋은 성능을 보여주었다.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.18-31
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• 2012

This paper proposes a matching engine of query-by-singing/humming (QbSH) system which retrieves the most similar music information by comparing the input data with the extracted feature information from polyphonic music like MP3. The feature sequences transcribed from polyphonic music may have many errors. So, to reduce the influence of errors and improve the performance, the chroma-scale representation, compensation and asymmetric DTW (Dynamic Time Warping) are adopted in the matching engine. The performance of various distance metrics are also investigated in this paper. In our experiment, the proposed QbSH system achieves MRR (Mean Reciprocal Rank) of 0.718 for 1000 singing/humming queries when searching from a database of 450 polyphonic musics.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1581-1592
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• 2009

Recently, we can hear various kinds of music in everywhere and anytime. If a user wants to find the music which was heard before in a street or cafe, but he does not know the title of the music, it is difficult to find it. That is the limitation of previous retrieval system of music data. To overcome these problems, we research a method of content-based retrieval of music data based on the recorded humming, the part of recorded music and the played musical instrument. In this paper, we investigated previous content-based retrieval methods of papers, systems and patents. Based on that, we research a method of content-based retrieval of music data. That is, in case of using the recorded humming and music for query, we extract the frequency information from the recorded humming/music and the stored music data by using FFT. We use a MIDI file in case of query by the played musical instrument. And by using dynamic programming matching, the error caused by the disparity of length between the input source with the stored music data could be reduced.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.2-7
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• 2004

In this paper, we proposed the content-based music information retrieval technique using some MPEG-7 low-level descriptors. Especially, pitch information and timbral features can be applied in music genre classification, music retrieval, or QBH(Query By Humming) because these can be modeling the stochasticpattern or timbral information of music signal. In this work, we restricted the music domain as O.S.T of movie or soap opera to apply broadcasting system. That is, the user can retrievalthe information of the unknown music using only an audio clip with a few seconds extracted from video content when background music sound greeted user's ear. We proposed the audio feature set organized by MPEG-7 descriptors and distance function by vector distance or ratio computation. Thus, we observed that the feature set organized by pitch information is superior to timbral spectral feature set and IFCR(Intra-Feature Component Ratio) is better than ED(Euclidean Distance) as a vector distance function. To evaluate music recognition, k-NN is used as a classifier

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• Animal Systematics, Evolution and Diversity
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• v.36 no.1
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• pp.78-80
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• 2020

A sphingid species, Macroglossum corythus Walker, 1856 is newly recorded from Korea. Macroglossum corythus can be characterized by grayish or dark greenish fore- and hindwings, blackish basal, ante- and postmedial lines with a blackish discal dot on the forewing and transverse antemedial line, as well as a strongly dentate postmedial line and a thick, dark brownish tinged subterminal line on the hindwing. The male genitalia of Macroglossum corythus can be characterized by the long saccular process of valva and the phallus with one hooked distal process and the large penshaped cornutus and a notched ridge. The female genitalia of Macroglossum corythus can be characterized by the long tubular ductus bursae and the long ovate corpus bursae with multiple stripes and a long triangular patch of minute signa. To date, six species of the genus Macroglossum have been identified in Korea.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1075-1080
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• 2009

이 글은 Music Information Retrieval(MIR) 기법을 사용하여 오디오 콘텐츠의 리듬 정보를 자동으로 분석하고 이를 시각화하는 방법에 대해 다룬다. 특히 MIR을 활용한 간단한 시각화(sound visualization) 어플리케이션을 소개함으로써 음악 정보 분석이 디자인, 시각 예술에서 다양하게 활용될 수 있음을 보이고자 한다. 음악적 정보를 시각 예술로 담아내려는 시도는 20세기 초 아방가르드 화가들에 의해 본격적으로 시작되었다. 80년대 이후에는 컴퓨터 기술의 급속한 발전으로 사운드와 이미지를 디지털 영역에서 쉽게 하나로 다룰 수 있게 되었고, 이에 따라 다양한 오디오 비주얼 예술작품들이 등장하였다. MIR은 오디오 콘텐츠로부터 음악적 정보를 분석하는 DSP(Digital Signal Processing) 기술로 최근 디지털 콘텐츠 시장의 확장과 더불어 연구가 활발히 진행되고 있다. 특히 웹이나 모바일에서는 이미 다양한 상용 어플리케이션이 적용되고 있는데 query-by-humming과 같은 음악 인식 어플리케이션이 대표적인 경우이다. 이 글에서는 onset 검출(onset detection)을 중심으로 음악적 리듬을 분석하는 알고리즘을 살펴보고 기본적인 조형원리에 따라 이를 시각화하는 어플리케이션의 예를 소개한다.

• Phonetics and Speech Sciences
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• v.4 no.1
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• pp.77-87
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• 2012

Interest in music recognition has been growing dramatically after NHN and Daum released their mobile applications for music recognition in 2010. Methods in music recognition based on audio analysis fall into two categories: music recognition using audio fingerprint and Query-by-Singing/Humming (QBSH). While music recognition using audio fingerprint receives music as its input, QBSH involves taking a user-hummed melody. In this paper, research trends are described for music recognition using audio fingerprint, focusing on two methods: one based on fingerprint generation using energy difference between consecutive bands and the other based on hash key generation between peak points. Details presented in the representative papers of each method are introduced.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.445-451
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• 2010

This paper presents a threshold adaptation based voice query transcription scheme for music information retrieval. The proposed scheme analyzes monophonic voice signal and generates its transcription for diverse music retrieval applications. For accurate transcription, we propose several advanced features including (i) Energetic Feature eXtractor (EFX) for onset, peak, and transient area detection; (ii) Modified Windowed Average Energy (MWAE) for defining multiple small but coherent windows with local threshold values as offset detector; and finally (iii) Circular Average Magnitude Difference Function (CAMDF) for accurate acquisition of fundamental frequency (F0) of each frame. In order to evaluate the performance of our proposed scheme, we implemented a prototype music transcription system called AMT2 (Automatic Music Transcriber version 2) and carried out various experiments. In the experiment, we used QBSH corpus [1], adapted in MIREX 2006 contest data set. Experimental result shows that our proposed scheme can improve the transcription performance.