Ideal Binary Mask Ratio: a novel metric for assessing binary-mask-based sound source separation algorithms
Hummersone, C, Mason, R and Brookes, T (2011) Ideal Binary Mask Ratio: a novel metric for assessing binary-mask-based sound source separation algorithms IEEE Transactions on Audio, Speech and Language Processing, 19 (7). pp. 2039-2045.
TASL-2011-2109380.pdf - Accepted version Manuscript
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A number of metrics has been proposed in the literature to assess sound source separation algorithms. The addition of convolutional distortion raises further questions about the assessment of source separation algorithms in reverberant conditions as reverberation is shown to undermine the optimality of the ideal binary mask (IBM) in terms of signal-to-noise ratio (SNR). Furthermore, with a range of mixture parameters common across numerous acoustic conditions, SNR–based metrics demonstrate an inconsistency that can only be attributed to the convolutional distortion. This suggests the necessity for an alternate metric in the presence of convolutional distortion, such as reverberation. Consequently, a novel metric—dubbed the IBM ratio (IBMR)—is proposed for assessing source separation algorithms that aim to calculate the IBM. The metric is robust to many of the effects of convolutional distortion on the output of the system and may provide a more representative insight into the performance of a given algorithm.
|Divisions :||Faculty of Arts and Social Sciences > School of Arts > Sound Recording|
|Date :||September 2011|
|Identification Number :||https://doi.org/10.1109/TASL.2011.2109380|
|Related URLs :|
|Depositing User :||Symplectic Elements|
|Date Deposited :||08 Sep 2011 09:07|
|Last Modified :||23 Sep 2013 18:45|
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