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MINUET Musical Interference Unmixing Estimation Technique Scott Rickard, Conor Fearon Department of Electronic & Electrical Engineering University College.

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Presentation on theme: "MINUET Musical Interference Unmixing Estimation Technique Scott Rickard, Conor Fearon Department of Electronic & Electrical Engineering University College."— Presentation transcript:

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2 MINUET Musical Interference Unmixing Estimation Technique Scott Rickard, Conor Fearon Department of Electronic & Electrical Engineering University College Dublin, Ireland Radu Balan, Justinian Rosca Siemens Corporate Research, Princeton, NJ. CISS0418 th March 2004

3 MINUET: The Problem Given x and n’ Find s

4 Classical Solution (Adaptive Filtering)

5 Adaptive Algorithms Least-Mean Square (LMS) Algorithm - minimises mean-square error Recursive Least Squares (RLS) Algorithm - minimises sum of squares of error

6 Problem! Performance drastically deteriorates with small phase and synchronisation errors.  Mixture: No error: Delayed by 1 sample: Delayed by 10 samples:

7 W-Disjoint Orthogonality At every point in the t-f representation of a mixture, only one source is active.

8 MINUET Solution  Consider simple problem:  Create Mask:  Solution:

9 Synchronisation Errors? The performance of time-frequency masking with respect to small phase and synchronisation errors is extremely robust.  Mixture: No error: Delayed by 1 sample: Delayed by 10 samples:

10 SNR improvement

11 Performance Measures SNR is a standard performance measure But what about speech quality? Incorrect partitioning of t-f domain reduces intelligibility of output. Introduce measure of WDO: O. Yilmaz and S. Rickard, "Blind Separation of Speech Mixtures via Time- Frequency Masking", IEEE Transactions on Signal Processing, To appear, July 2004.

12 WDO

13 MINUET Channel Estimate Find set of t-f points, S, such that for

14 Adaptive Testing AlgorithmSNR (dB)WDO NLMS0.540.12 RLS10.110.9 MINUET15.180.76 AlgorithmSNR (dB)WDO NLMS-7.46-4.57 RLS-1.36-0.38 MINUET7.690.44 Unity Channel: Random Channel:

15 Conclusions and Future Work MINUET estimates the channel and removes interference using instantaneous t-f magnitudes only. This creates extraordinary robustness to phase errors when compared to classical adaptive filtering methods. Improvements in t-f masking still necessary to increase intelligibility. Algorithm complexity has not yet been considered. We presented pilot tests serving as proof of concept only. More realistic testing must be done to genuinely assess performance. MINUET will be effective for any signals which are WDO.

16 Thank you for your attention!


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