Convolutive Blind Sparse Source Separation with Application to EMG Decomposition
Bibliographic record
Abstract
Electromyogram (EMG) signal decomposition is a special case of convolutive blind source separation (CBSS) problem.Specific characteristics of EMG signals and their corresponding channel matrix leads us to proposing family of algorithms using multidomain sparsity of EMG signals and low rank structure of channel matrix. Sparsity of source signals plays an important role in CBSS.In this thesis we propose a family of algorithms for CBSS that exploits sparse source signals in multiple domains.Our approach is to jointly estimate the sparse vector of concatenated sources and the mixing matrix.We propose CBSS algorithms which are composed of two steps for multi domain sparse vector recovery and mixing matrix estimation.In particular, we propose two efficient algorithms coined multi domain adaptive thresholding (MDAT) for both under-determined and over-determined cases and multi domain blind approximate message passing (MDBAMP) for multi-channel convolutive blind sparse source separation for under-determined cases, when the source signals are sparse in multiple domains.Using their sparse representations in all sparse domains helps in estimating the sources more precisely with fewer number of measurements compared to specific domain sparsity CBSS methods such as morphological component analysis as the most general and best performing algorithm of its own category.Simulation results in EMG signal decomposition are illustrating the superior performance of proposed algorithms in terms of normalized mean square errors of the estimations compared to morphological component analysis algorithm as the best existing algorithm for the sparse blind source separation.In multichannel source separation, channels are usually correlated.Therefore, in this thesis, we propose a CBSS algorithm that exploits both low rank channel matrix and sparse source signals in the time domain.Our approach is to jointly estimate the sparse vector of concatenated sources and low rank channel matrix.In sparse vector recovery and low rank matrix estimation algorithms, the mixing matrix and the linear operator of low rank matrix are known.Here, we propose
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".