MINUETMINUETMusical Interference Unmixing Musical Interference Unmixing

Estimation TechniqueEstimation Technique

Scott Rickard, Conor Fearon

Department of Electronic & Electrical Engineering

University College Dublin, Ireland

Radu Balan, Justinian Rosca

Siemens Corporate Research,

Princeton, NJ.

CISS0418th March 2004

MINUET: The ProblemMINUET: The Problem

Given x and n’ Find s

Classical SolutionClassical Solution(Adaptive Filtering)(Adaptive Filtering)

Adaptive AlgorithmsAdaptive Algorithms

Least-Mean Square (LMS) Algorithm

- minimises mean-square error

Recursive Least Squares (RLS) Algorithm

- minimises sum of squares of error

Problem!Problem!Performance drastically deteriorates with

small phase and synchronisation errors. Mixture:

• No error:

• Delayed by 1 sample:

• Delayed by 10 samples:

W-Disjoint OrthogonalityW-Disjoint Orthogonality

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

MINUET SolutionMINUET Solution Consider simple problem:

Create Mask:

Solution:

),('),( nx otherwise

Synchronisation Errors?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:

SNR improvementSNR improvement

Performance MeasuresPerformance Measures

SNR is a standard performance measureBut 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.

WDOWDO

MINUET Channel EstimateMINUET Channel EstimateFind set of t-f points, S, such that

for

otherwise

),(')(),( nHx

Adaptive TestingAdaptive Testing

Algorithm SNR (dB) WDO

NLMS 0.54 0.12

RLS 10.11 0.9

MINUET 15.18 0.76

Algorithm SNR (dB) WDO

NLMS -7.46 -4.57

RLS -1.36 -0.38

MINUET 7.69 0.44

Unity Channel:

Random Channel:

Conclusions and Future WorkConclusions 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.

Thank you for your attention!Thank you for your attention!