Evaluating Source Separation Algorithms With Reverberant Speech

Mandel, Michael I.; Bressler, Scott; Shinn‐Cunningham, Barbara; Ellis, Daniel P. W.

doi:10.1109/tasl.2010.2052252

Cited by 23 publications

(13 citation statements)

References 34 publications

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“…Stereo to multichannel upmix [38] is also a vibrant area of research to which advanced source separation systems could contribute and for which novel performance criteria are needed. Appropriate statistical confidence measures, tighter oracle performance bounds and advanced diagnosis procedures such as those in [39,40,41] are also needed to increase the insight that can be gained from evaluation. Finally, increased publicity and networking efforts should be made to promote source separation evaluations in the biomedical signal processing community, as well as in other communities, e.g.…”

Section: Evaluation Methodologymentioning

confidence: 99%

The signal separation evaluation campaign (2007–2010): Achievements and remaining challenges

et al. 2012

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We present the outcomes of three recent evaluation campaigns in the field of audio and biomedical source separation. These campaigns have witnessed a boom in the range of applications of source separation systems in the last few years, as shown by the increasing number of datasets from 1 to 9 and the increasing number of submissions from 15 to 34. We first discuss their impact on the definition of a reference evaluation methodology, together with shared datasets and software. We then present the key results obtained over almost all datasets. We conclude by proposing directions for future research and evaluation, based in particular on the ideas raised during the related panel discussion at the Ninth International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2010).

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Section: Evaluation Methodologymentioning

confidence: 99%

The signal separation evaluation campaign (2007–2010): Achievements and remaining challenges

et al. 2012

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“…The PESQ score is computed by a comparison of the original (unmixed, anechoic) speech source signal to the recovered signal estimate. Both signals are time-aligned and passed through an auditory transform to achieve a psychoacoustically motivated representation [55]. The differences between the signals in this representation are measured and used to provide an estimate of the distortion in the signal estimate.…”

Section: Pesqmentioning

confidence: 99%

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

Jafari

Haque

Togneri

et al. 2013

EURASIP J. Adv. Signal Process.

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The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments.

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“…A recent study [5] has suggested a metric for assessing the separation of reverberated speech. The metric, termed direct-path, early echoes, and reverberation of target and masker (DERTM), measures the suppression of the direct sound, early reflections and late reverberation of both the target and interfering sounds.…”

Section: Ideal Binary Masks and Metricsmentioning

confidence: 99%

Ideal Binary Mask Ratio: A Novel Metric for Assessing Binary-Mask-Based Sound Source Separation Algorithms

Hummersone

Mason

Brookes

2011

IEEE Trans. Audio Speech Lang. Process.

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Abstract-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.

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Evaluating Source Separation Algorithms With Reverberant Speech

Cited by 23 publications

References 34 publications

The signal separation evaluation campaign (2007–2010): Achievements and remaining challenges

The signal separation evaluation campaign (2007–2010): Achievements and remaining challenges

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

Ideal Binary Mask Ratio: A Novel Metric for Assessing Binary-Mask-Based Sound Source Separation Algorithms

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