• 제목/요약/키워드: Music Engineering

검색결과 611건 처리시간 0.03초

Music summarization using visual information of music and clustering method

  • Kim, Sang-Ho;Ji, Mi-Kyong;Kim, Hoi-Rin
    • 한국HCI학회:학술대회논문집
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    • 한국HCI학회 2006년도 학술대회 1부
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    • pp.400-405
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    • 2006
  • In this paper, we present effective methods for music summarization which summarize music automatically. It could be used for sample music of on-line digital music provider or some music retrieval technology. When summarizing music, we use different two methods according to music length. First method is for finding sabi or chorus part of music which can be regarded as the most important part of music and the second method is for extracting several parts which are in different structure or have different mood in the music. Our proposed music summarization system is better than conventional system when structure of target music is explicit. The proposed method could generate just one important segment of music or several segments which have different mood in the music. Thus, this scheme will be effective for summarizing music in several applications such as online music streaming service and sample music for Tcommerce.

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Automatic Music Recommendation System based on Music Characteristics

  • Kim, Sang-Ho;Kim, Sung-Tak;Kwon, Suk-Bong;Ji, Mi-Kyong;Kim, Hoi-Rin;Yoon, Jeong-Hyun;Lee, Han-Kyu
    • 한국HCI학회:학술대회논문집
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    • 한국HCI학회 2007년도 학술대회 1부
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    • pp.268-273
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    • 2007
  • In this paper, we present effective methods for automatic music recommendation system which automatically recommend music by signal processing technology. Conventional music recommendation system use users’ music downloading pattern, but the method does not consider acoustic characteristics of music. Sometimes, similarities between music are used to find similar music for recommendation in some method. However, the feature used for calculating similarities is not highly related to music characteristics at the system. Thus, our proposed method use high-level music characteristics such as rhythm pattern, timbre characteristics, and the lyrics. In addition, our proposed method store features of music, which individuals queried, to recommend music based on individual taste. Experiments show the proposed method find similar music more effectively than a conventional method. The experimental results also show that the proposed method could be used for real-time application since the processing time for calculating similarities between music, and recommending music are fast enough to be applicable for commercial purpose.

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다중 재머 환경에서 DOA 추정 성능 개선을 위한 Root-assisted MUSIC 알고리즘 (Root-assisted MUSIC algorithm for the efficient DOA estimation in Multi-Jammer Environments)

  • 이주현;최헌호;최윤섭;임덕원;박찬식;이상정
    • 한국항행학회논문지
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    • 제17권4호
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    • pp.386-395
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    • 2013
  • 본 논문에서는 AOA (Angle of Arrival)기반의 재머위치 결정기법에 사용되는 DOA 추정 알고리즘을 대상으로 Root-MUSIC으로 1차 DOA 추정을 수행한 후 MUSIC을 이용한 2차 DOA 추정을 수행하는 성능 개선 알고리즘을 제시하였다. 또한 제안한 알고리즘을 검증하기 위해 소프트웨어 기반의 시뮬레이션 플랫폼을 구성하여 오차환경에 따른 DOA 추정 성능 분석을 수행하고, 본 논문에서 제시한 DOA 추정 알고리즘의 DOA 추정 정확도 및 분해능의 개선 정도를 분석하였다.

다중레벨 벡터양자화 기반의 유사도를 이용한 자동 음악요약 (Automatic Music Summarization Using Similarity Measure Based on Multi-Level Vector Quantization)

  • 김성탁;김상호;김회린
    • The Journal of the Acoustical Society of Korea
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    • 제26권2E호
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    • pp.39-43
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    • 2007
  • Music summarization refers to a technique which automatically extracts the most important and representative segments in music content. In this paper, we propose and evaluate a technique which provides the repeated part in music content as music summary. For extracting a repeated segment in music content, the proposed algorithm uses the weighted sum of similarity measures based on multi-level vector quantization for fixed-length summary or optimal-length summary. For similarity measures, count-based similarity measure and distance-based similarity measure are proposed. The number of the same codeword and the Mahalanobis distance of features which have same codeword at the same position in segments are used for count-based and distance-based similarity measure, respectively. Fixed-length music summary is evaluated by measuring the overlapping ratio between hand-made repeated parts and automatically generated ones. Optimal-length music summary is evaluated by calculating how much automatically generated music summary includes repeated parts of the music content. From experiments we observed that optimal-length summary could capture the repeated parts in music content more effectively in terms of summary length than fixed-length summary.

Automatic Music Summarization Using Vector Quantization and Segment Similarity

  • Kim, Sang-Ho;Kim, Sung-Tak;Kim, Hoi-Rin
    • The Journal of the Acoustical Society of Korea
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    • 제27권2E호
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    • pp.51-56
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    • 2008
  • In this paper, we propose an effective method for music summarization which automatically extracts a representative part of the music by using signal processing technology. Proposed method uses a vector quantization technique to extract several segments which can be regarded as the most important contents in the music. In general, there is a repetitive pattern in music, and human usually recognizes the most important or catchy tune from the repetitive pattern. Thus the repetition which is extracted using segment similarity is considered to express a music summary. The segments extracted are again combined to generate a complete music summary. Experiments show the proposed method captures the main theme of the music more effectively than conventional methods. The experimental results also show that the proposed method could be used for real-time application since the processing time in generating music summary is much faster than other methods.

Design of Music Learning Assistant Based on Audio Music and Music Score Recognition

  • Mulyadi, Ahmad Wisnu;Machbub, Carmadi;Prihatmanto, Ary S.;Sin, Bong-Kee
    • 한국멀티미디어학회논문지
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    • 제19권5호
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    • pp.826-836
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    • 2016
  • Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

뉴턴 반복을 이용한 AD-MUSIC 알고리즘 성능향상 (Performance Improvement of AD-MUSIC Algorithm Using Newton Iteration)

  • 백지웅;김종만;이준호
    • 한국전자파학회논문지
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    • 제28권11호
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    • pp.880-885
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    • 2017
  • 기존에 제안된 AD-MUSIC 알고리즘을 이용하여 2차원 탐색 없이 1차원 탐색을 반복함으로써 DOD/DOA 추정이 가능하다. 본 논문에서는 계산량을 더욱 감소하기 위해 1차원 탐색에 Newton 기반 기법을 적용한다. 본 논문은 바이스태틱 MIMO 레이다 시스템의 수신신호 모델링과 AD-MUSIC의 유도과정을 보이고, 뉴턴 반복 기법을 AD-MUSIC에 적용한다. 추정 시, 기존의 AD-MUSIC 알고리즘의 성능과 계산량이 탐색 간격에 영향을 받는 것에 반해, AD-MUSIC의 성능과 뉴턴기법을 적용하는 본 논문의 방법인 경우, 탐색 간격에 관계없이 우수한 성능을 보이고, 계산량 또한 감소하는 효과를 보인다는 것을 시뮬레이션을 통해 보인다.

A Method for Measuring the Difficulty of Music Scores

  • Song, Yang-Eui;Lee, Yong Kyu
    • 한국컴퓨터정보학회논문지
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    • 제21권4호
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    • pp.39-46
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    • 2016
  • While the difficulty of the music can be classified by a variety of standard, conventional methods are classified by the subjective judgment based on the experience of many musicians or conductors. Music score is difficult to evaluate as there is no quantitative criterion to determine the degree of difficulty. In this paper, we propose a new classification method for determining the degree of difficulty of the music. In order to determine the degree of difficulty, we convert the score, which is expressed as a traditional music score, into electronic music sheet. Moreover, we calculate information about the elements needed to play sheet music by distance of notes, tempo, and quantifying the ease of interpretation. Calculating a degree of difficulty of the entire music via the numerical data, we suggest the difficulty evaluation of the score, and show the difficulty of music through experiments.

Backpropagation을 이용한 악보인식 (Recognition of Music using Backpropagation Network)

  • 박현준;차의영
    • 한국정보통신학회논문지
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    • 제11권6호
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    • pp.1170-1175
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    • 2007
  • 본 논문에서는 신경회로망 알고리즘 중 하나인 backpropagation network을 이용한 악보인식 기법과 그에 필요한 악보 영상에 대한 전처리 기법을 제안한다. 전처리과정으로 이진화, 기울기 보정, 오선제거 등의 과정을 수행하여 인식에 필요한 음악 기호와 음표를 분리한다. 분리된 음악 기호와 음표들은 backpropagation 알고리즘을 사용하여 구성된 음표 인식 신경망과 비음표 인식 신경망을 통해 각각 음표와 비음표 인식과정을 거친다. 다양한 복잡도를 가진 악보를 대상으로 한 실험 및 분석 결과를 통해 제안한 악보 인식 기법의 정확도를 기술하였다.

Ranking Tag Pairs for Music Recommendation Using Acoustic Similarity

  • Lee, Jaesung;Kim, Dae-Won
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제15권3호
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    • pp.159-165
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    • 2015
  • The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.