INFORMED SOURCE SEPARATION VIA COMPRESSIVE GRAPH SIGNAL SAMPLING

被引:0
作者
Puy, Gilles [1 ]
Ozerov, Alexey [1 ]
Duong, Ngoc Q. K. [1 ]
Perez, Patrick [1 ]
机构
[1] Technicolor, 975 Ave Champs Blancs, F-35576 Cesson Sevigne, France
来源
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年
关键词
Informed source separation; audio object coding; non-negative matrix factorisation; graph signal processing; compressive sampling; BLIND;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-frequency point, we compute an ideal map indicating which source is active at each time-frequency point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees accurate and stable recovery at the decoder. The graph is built using feature vectors, computed using non-negative matrix factorization, that allows us to connect similar source activations in the time-frequency plane. We show that the proposed approach performs better than state-of-the-art methods at low bitrate.
引用
收藏
页码:1 / 5
页数:5
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