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In this paper, we present a simple and fast method to separate a monaural audio signal into harmonic and percussive components, which is much useful for multi-pitch analysis, automatic music transcription, drum detection, modification of music, and so on. Exploiting the differences in the spectrograms of harmonic and percussive components, the objective(More)
In this paper, we propose a new approach to sparseness-based BSS based on the EM algorithm, which iteratively estimates the DOA and the time-frequency mask for each source through the EM algorithm under the sparseness assumption. Our method has the following characteristics: 1) it enables the introduction of physical observation models such as the diffuse(More)
  • Nobutaka Ono
  • 2011
This paper presents stable and fast update rules for independent vector analysis (IVA) based on auxiliary function technique. The algorithm consists of two alternative updates: 1) weighted covariance matrix updates and 2) demixing matrix updates, which include no tuning parameters such as step size. The monotonic decrease of the objective function at each(More)
This paper presents a new sparse representation for acoustic signals which is based on a mixing model defined in the complex-spectrum domain (where additivity holds), and allows us to extract recurrent patterns of magnitude spectra that underlie observed complex spectra and the phase estimates of constituent signals. An efficient iterative algorithm is(More)
In this paper, we present a real-time equalizer to control a volume balance of harmonic and percussive components in music signals without a priori knowledge of scores or included instruments. The harmonic and percussive components of music signals have much different structures in the power spectrogram domain, the former is horizontal, while the latter is(More)
Estimation of melody line in homophonic music audio signals is a challenging subject of study. Some of the difficulties are derived from presence of accompanying components. To overcome those difficulties, we propose a method to enhance melodic components in music audio signals. The enhancement algorithm uses fluctuation and shortness of melodic components,(More)
In this paper, aiming to utilize independent recording devices as a distributed microphone array, we present a novel method for alignment of recorded signals with localizing microphones and sources. Unlike conventional microphone array, signals recorded by independent devices have different origins of time, and microphone positions are generally unknown. In(More)
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(More)
This paper presents a new multiplicative algorithm for non-negative matrix factorization with β-divergence. The derived update rules have a similar form to those of the conventional multiplicative algorithm, only differing through the presence of an exponent term depending on β. The convergence is theoretically proven for any real-valued β based on the(More)
Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output(More)