#### Filter Results:

- Full text PDF available (133)

#### Publication Year

2003

2017

- This year (14)
- Last 5 years (82)
- Last 10 years (136)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Hirokazu Kameoka, Takuya Nishimoto, Shigeki Sagayama
- IEEE Transactions on Audio, Speech, and Language…
- 2007

This paper proposes a multipitch analyzer called the harmonic temporal structured clustering (HTC) method, that jointly estimates pitch, intensity, onset, duration, etc., of each underlying source in a multipitch audio signal. HTC decomposes the energy patterns diffused in time-frequency space, i.e., the power spectrum time series, into distinct clusters… (More)

- Hirokazu Kameoka, Nobutaka Ono, Kunio Kashino, Shigeki Sagayama
- 2009 IEEE International Conference on Acoustics…
- 2009

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)

- Hirokazu Kameoka, Tomohiro Nakatani, Takuya Yoshioka
- 2009 IEEE International Conference on Acoustics…
- 2009

This paper presents a blind dereverberation method designed to recover the subband envelope of an original speech signal from its reverberant version. The problem is formulated as a blind deconvolution problem with non-negative constraints, regularized by the sparse nature of speech spectrograms. We derive an iterative algorithm for its optimization, which… (More)

- Nobutaka Ono, Kenichi Miyamoto, Jonathan Le Roux, Hirokazu Kameoka, Shigeki Sagayama
- 2008 16th European Signal Processing Conference
- 2008

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)

- Hiroshi Sawada, Hirokazu Kameoka, Shoko Araki, Naonori Ueda
- IEEE Transactions on Audio, Speech, and Language…
- 2013

This paper presents new formulations and algorithms for multichannel extensions of non-negative matrix factorization (NMF). The formulations employ Hermitian positive semidefinite matrices to represent a multichannel version of non-negative elements. Multichannel Euclidean distance and multichannel Itakura-Saito (IS) divergence are defined based on… (More)

- Shoichiro Saito, Hirokazu Kameoka, Keigo Takahashi, Takuya Nishimoto, Shigeki Sagayama
- IEEE Transactions on Audio, Speech, and Language…
- 2008

This paper introduces a new music signal processing method to extract multiple fundamental frequencies, which we call specmurt analysis. In contrast with cepstrum which is the inverse Fourier transform of log-scaled power spectrum with linear frequency, specmurt is defined as the inverse Fourier transform of linear power spectrum with log-scaled frequency.… (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)

- Masahiro Nakano, Jonathan Le Roux, Hirokazu Kameoka, Tomohiko Nakamura, Nobutaka Ono, Shigeki Sagayama
- 2011 IEEE Workshop on Applications of Signal…
- 2011

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)

- Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, Hiroshi Saruwatari
- IEEE/ACM Transactions on Audio, Speech, and…
- 2016

This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been… (More)

- Akisato Kimura, Hirokazu Kameoka, +4 authors Katsuhiko Ishiguro
- ICPR
- 2010

Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired… (More)