The 2011 Signal Separation Evaluation Campaign (SiSEC2011): - Audio Source Separation -

Araki, Shoko; Nesta, Francesco; Vincent, Emmanuel; Koldovsky, Zbynek; Nolte, Guido; Ziehe, Andreas; Benichoux, Alexis

doi:10.1007/978-3-642-28551-6_51

Cited by 91 publications

(68 citation statements)

References 22 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impulse responses were measured by using a maximum length sequence generated by a 17-th order polynomial over GF (2). We made four sets of mixtures using the music sources listed in Table I, which can be found at the professionally produced music recordings page of the Signal Separation Evaluation Campaign (SiSEC 2011) [24]. The mixtures were down-sampled to 16 kHz.…”

Section: A Experimental Setupsmentioning

confidence: 99%

Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

Sawada

Kameoka

Araki

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

Self Cite

246

287

View full text Add to dashboard Cite

Abstract-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 appropriate statistical models utilizing multivariate complex Gaussian distributions. To minimize this distance/divergence, efficient optimization algorithms in the form of multiplicative updates are derived by using properly designed auxiliary functions. Two methods are proposed for clustering NMF bases according to the estimated spatial property. Convolutive blind source separation (BSS) is performed by the multichannel extensions of NMF with the clustering mechanism. Experimental results show that 1) the derived multiplicative update rules exhibited good convergence behavior, and 2) BSS tasks for several music sources with two microphones and three instrumental parts were evaluated successfully.

show abstract

Section: A Experimental Setupsmentioning

confidence: 99%

Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

Sawada

Kameoka

Araki

et al. 2013

IEEE Trans. Audio Speech Lang. Process.

Self Cite

246

287

View full text Add to dashboard Cite

show abstract

“…These dry sources were obtained from professionally produced music and underdetermined separation tasks in SiSEC2011 [29]. To simulate the reverberant mixture, the observed signals were produced by convoluting the impulse response E2A (T 60 = 300 ms) or JR2 (T 60 = 470 ms), which was obtained from the RWCP [30], with each source.…”

Section: B Dataset and Experimental Conditionsmentioning

confidence: 99%

Experimental analysis of optimal window length for independent low-rank matrix analysis

Kitamura

Saruwatari

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-In this paper, we address the blind source separation (BSS) problem and analyze the optimal window length in the short-time Fourier transform (STFT) for independent low-rank matrix analysis (ILRMA). ILRMA is a state-of-the-art BSS technique that utilizes the statistical independence between low-rank matrix spectrogram models, which are estimated by nonnegative matrix factorization. In conventional frequency-domain BSS, the modeling error of a mixing system increases when the window length is too short, and the accuracy of statistical estimation decreases when the window length is too long. Therefore, the optimal window length is determined by both the reverberation time and the number of time frames. However, unlike classical BSS methods such as ICA and IVA, ILRMA enables the full modeling of spectrograms, which may improve the robustness to a decrease in the number of frames in a longer-window case. To confirm this hypothesis, the optimal window length for ILRMA is experimentally investigated, and the difference between the performances of ILRMA and conventional BSS is discussed.

show abstract

“…The proposed algorithm was evaluated in terms of mono audio source separation using six mixtures, from the "underdetermined speech and music mixtures task"of the Signal Separation Evaluation Campaign 2011 (SiSEC 2011) [18], which is also included in SiSEC 2016, with downmixing to a mono signal. Each mixture was composed of three sources in 16-bit format with a sampling frequency of 16000 Hz.…”

Section: Unsupervised Audio Source Separation Testmentioning

confidence: 99%

Low-Complexity Recursive-Least-Squares-Based Online Nonnegative Matrix Factorization Algorithm for Audio Source Separation

Lee

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYAn online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity. key words: nonnegative matrix factorization (NMF), online nonnegatie matrix factorization (ONMF), recursive least squares (RLS), low complexity, audio source separation

show abstract

The 2011 Signal Separation Evaluation Campaign (SiSEC2011): - Audio Source Separation -

Cited by 91 publications

References 22 publications

Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

Experimental analysis of optimal window length for independent low-rank matrix analysis

Low-Complexity Recursive-Least-Squares-Based Online Nonnegative Matrix Factorization Algorithm for Audio Source Separation

Contact Info

Product

Resources

About