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Non-negative matrix factorization (NMF) is attracting a lot of attention as a powerful technique for music transcription and audio source separation. With this approach, the magnitude (or power) spectrogram of a mixed signal, interpreted as non-negative matrix Y, is factorized into the product of two non-negative matrices, dictionary matrix H and activation matrix U. Each template vector in the dictionary...
In this paper, we propose a new hybrid method that concatenates directional clustering and advanced nonnegative matrix factorization (NMF) for the purpose of the specific sound extraction from the multichannel music signal. Multichannel music signal separation technology is aimed to extract a specific target signal from observed multichannel signals that contain multiple instrumental sounds. In the...
In this paper, we address an optimization issue for the divergence in supervised nonnegative matrix factorization with spectrogram restoration, which has been proposed for addressing multichannel signal separation. This method separates non-target components and reconstructs some missing data caused by preceding spatial clustering via supervised basis extrapolation. In our previous study, we only...
This paper describes a method to separate a monaural music signal into harmonic components e.g., a guitar and percussive components, e.g., a snare drum. Separation of these two components is a useful preprocessing for many music information retrieval applications, and in addition, it can be used as a new kind of music equalizer in itself, which enables a music listener to adjust the ratio of the volume...
Music spectrograms typically have many structural regularities that can be exploited to help solve the problem of decomposing a given spectrogram into distinct musically meaningful components. In this paper, we introduce new variants of the non-negative matrix factorization concept that incorporate music-specific constraints.
This paper proposes model-based non-negative matrix factorization (NMF) for estimating basis spectra and activations, detecting note onsets and offsets, and determining beat locations, simultaneously. Multipitch analysis is a process of detecting the pitch and onset of each note from a musical signal. Conventional NMF-based approaches often lead to unsatisfactory results very possibly due to the lack...
This paper presents a Bayesian nonparametric latent source discovery method for music signal analysis. In audio signal analysis, an important goal is to decompose music signals into individual notes, with applications such as music transcription, source separation or note-level manipulation. Recently, the use of latent variable decompositions, especially nonnegative matrix factorization (NMF), has...
This paper presents a nonparametric Bayesian extension of nonnegative matrix factorization (NMF) for music signal analysis. Instrument sounds often exhibit non-stationary spectral characteristics. We introduce infinite-state spectral bases into NMF to represent time-varying spectra in polyphonic music signals. We describe our extension of NMF with infinite-state spectral bases generated by the Dirichlet...
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