Robust distributed multi-speaker voice activity detection using stability selection for sparse non-negative feature extraction

Hamaidi, L. Khadidja; Muma, Michael; Zoubir, Abdelhak M.

doi:10.23919/eusipco.2017.8081189

Cited by 11 publications

(4 citation statements)

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“…[86][87][88] Further, by focusing exclusively on prediction error, most feature selection approaches fail to consider that their solutions may be difficult to interpret because different subsets of features can result in similar prediction error. In contrast, feature stability is an indicator of biomarker reproducibility, 85 and stability-based feature selection methods have been highly successful in microarray analysis and chemometrics, [89][90][91][92] as well as other applications 93 . Here, we contribute to this body of work in the context of lesion mapping, showing that the identification of stable features can improve mod- A small group of neuroimaging studies have previously leveraged stability analysis with success outside of lesion mapping and our approach may be relevant to other modalities.…”

Section: Improving Models Trained On Neuroimaging Data Through Identi...mentioning

confidence: 99%

Stable multivariate lesion symptom mapping

Teghipco,

Newman-Norlund,

Gibson

et al. 2024

Aperture Neuro

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Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness—considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

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Section: Improving Models Trained On Neuroimaging Data Through Identi...mentioning

confidence: 99%

Stable multivariate lesion symptom mapping

Teghipco,

Newman-Norlund,

Gibson

et al. 2024

Aperture Neuro

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“…Stability Selection is a very powerful method that can be applied to Lasso or Graphical Lasso models (Meinshausen and Bühlmann [2010]), but which also has been extended to Boosting models in Hofner et al [2015]. The Stability Selection has been applied successfully in the context of gene expression (Meinshausen and Bühlmann [2010], Stekhoven et al [2012], Shah and Samworth [2013], Hofner et al [2015]), fMRI data (Ryali et al [2012]) and voice activity detection (Hamaidi et al [2017]), which are exemplary for having very few observations with a huge number of predictors.…”

Section: Introductionmentioning

confidence: 99%

Loss-guided Stability Selection

Werner¹

2022

Preprint

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In modern data analysis, sparse model selection becomes inevitable once the number of predictors variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring expert knowledge to suitably configure the hyperparameters. Since model selection depends on the loss function, i.e., predictor sets selected w.r.t. some particular loss function differ from those selected w.r.t. some other loss function, we propose a Stability Selection variant which respects the chosen loss function via an additional validation step based on out-of-sample validation data, optionally enhanced with an exhaustive search strategy. Our Stability Selection variants are widely applicable and user-friendly. Moreover, our Stability Selection variants can avoid the issue of severe underfitting which affects the original Stability Selection for noisy high-dimensional data, so our priority is not to avoid false positives at all costs but to result in a sparse stable model with which one can make predictions. Experiments where we consider both regression and binary classification and where we use Boosting as model selection algorithm reveal a significant precision improvement compared to raw Boosting models while not suffering from any of the mentioned issues of the original Stability Selection.

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“…Single channel VAD has been extensively studied [4], [5], but not in the multichannel and the WASN cases. In [6], the VAD problem with WASN is formed as a node clustering problem first, and then the VAD is obtained locally at the clustered nodes. However, the development of distributed VAD method with global decision making strategy is not well studied.…”

Section: Introductionmentioning

confidence: 99%

“…The estimated speech PSD can be obtained in a similar way. Inserting (15) and the speech PSD estimate in (5) and (6), and with the distributed estimation of ln L G (ȳ(k, n)), the decision is made by using (10).…”

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confidence: 99%