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MINUET Musical Interference Unmixing Estimation Technique

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. 18 th March 2004. CISS04. MINUET: The Problem.

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MINUET Musical Interference Unmixing Estimation Technique

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  1. MINUETMusical 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. 18th March 2004 CISS04

  2. MINUET: The Problem • Given x and n’ • Find s

  3. Classical Solution(Adaptive Filtering)

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

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

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

  7. MINUET Solution • Consider simple problem: • Create Mask: • Solution:

  8. 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:

  9. SNR improvement

  10. 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.

  11. WDO

  12. MINUET Channel Estimate • Find set of t-f points, S, such that for

  13. Adaptive Testing Unity Channel: Random Channel:

  14. 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.

  15. Thank you for your attention!

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