Evaluation of blind signal separation method using directivity pattern under reverberant conditions

Kurita, Satoshi; Saruwatari, Hiroshi; Kajita, S.; Takeda, Kazuya; Itakura, Fumitada

doi:10.1109/icassp.2000.861203

Cited by 131 publications

(117 citation statements)

References 2 publications

Supporting

Mentioning

115

Contrasting

Unclassified

Order By: Relevance

“…One interesting approach is to consider the spatial arrangement of the source and microphones: a beamforming approach [17][18][19]. If most of the signal observed at the microphones arrives from the direction of the direct path from the source, the time delay between the microphones will correspond to the direction of arrival (DOA) of the source.…”

Section: Frequency-domain Icamentioning

confidence: 99%

See 1 more Smart Citation

An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

View full text Add to dashboard Cite

We consider the problem of convolutive blind source separation of stereo mixtures, where a pair of microphones records mixtures of sound sources that are convolved with the impulse response between each source and sensor. We propose an Adaptive Stereo Basis (ASB) source separation method for such convolutive mixtures, using an adaptive transform basis which is learned from the stereo mixture pair.The stereo basis vector pairs of the transform are grouped according to the estimated relative delay between the left and right channels for each basis, and the sources are then extracted by projecting the transformed signal onto the subspace corresponding to each group of basis vector pairs. The performance of the proposed algorithm is compared with FD-ICA and DUET under different reverberation and noise conditions, using both objective distortion measures and formal listening tests. Preprint submitted to Elsevier Science 10 August 2007NOTICE: this is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in : Neurocomputing 71(10-12), 2087-2097, June 2008. doi: 10.1016/j.neucom.2007 The results indicate that the proposed stereo coding method is competitive with both these algorithms at short and intermediate reverberation times, and offers significantly improved performance at low noise and short reverberation times.

show abstract

Section: Frequency-domain Icamentioning

confidence: 99%

“…If most of the signal observed at the microphones arrives from the direction of the direct path from the source, the time delay between the microphones will correspond to the direction of arrival (DOA) of the source. The source estimates can then be permuted so that their DOAs are aligned [17,18].…”

Section: Frequency-domain Icamentioning

confidence: 99%

An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

View full text Add to dashboard Cite

show abstract

“…This is the scaling problem. There are some methods to solve the permutation and scaling problems [12][13][14][15][16][17][18]. After solving the permutation and the scaling problem, the time-domain output signals () i ytare calculated with an inverse STFT (ISTFT) of the separated signals ( )…”

Section: Frequency-domain Convolutive Bssmentioning

confidence: 99%

“…For the scaling problem, in one method, the output is filtered by the inverse of the separation filter [12]. For the permutation problem, spatial information, such as the direction-of-arrivals (DOA) of sources, can be estimated and used [13,14]. Another method utilizes the coherency of the mixing matrices in several adjacent frequencies [15].…”

Section: Introductionmentioning

confidence: 99%

Convolutive ICA for Audio Signals

Geravanchizadeh¹,

Hesam²

2012

Independent Component Analysis for Audio and Biosignal Applications

View full text Add to dashboard Cite

“…The phase component of that vector contains geometrical information of the virtual sources. In [11], [16]- [20], this geometrical information was used to solve the permutation problem of FD-ICA.…”

Section: Grouping Virtual Signal Componentsmentioning

confidence: 99%