• 제목/요약/키워드: 리듬 음원 분리

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Blind Rhythmic Source Separation (블라인드 방식의 리듬 음원 분리)

  • Kim, Min-Je;Yoo, Ji-Ho;Kang, Kyeong-Ok;Choi, Seung-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.8
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    • pp.697-705
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    • 2009
  • An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.

Gaussian Processes for Source Separation: Pseudo-likelihood Maximization (유사-가능도 최대화를 통한 가우시안 프로세스 기반 음원분리)

  • Park, Sun-Ho;Choi, Seung-Jin
    • Journal of KIISE:Software and Applications
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    • v.35 no.7
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    • pp.417-423
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    • 2008
  • In this paper we present a probabilistic method for source separation in the case here each source has a certain temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can easily be computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. umerical experiments confirm the useful behavior of our method, compared to existing source separation methods.

Music Transcription Using Non-Negative Matrix Factorization (비음수 행렬 분해 (NMF)를 이용한 악보 전사)

  • Park, Sang-Ha;Lee, Seok-Jin;Sung, Koeng-Mo
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.2
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    • pp.102-110
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    • 2010
  • Music transcription is extracting pitch (the height of a musical note) and rhythm (the length of a musical note) information from audio file and making a music score. In this paper, we decomposed a waveform into frequency and rhythm components using Non-Negative Matrix Factorization (NMF) and Non-Negative Sparse coding (NNSC) which are often used for source separation and data clustering. And using the subharmonic summation method, fundamental frequency is calculated from the decomposed frequency components. Therefore, the accurate pitch of each score can be estimated. The proposed method successfully performed music transcription with its results superior to those of the conventional methods which used either NMF or NNSC.