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Found 6 papers, 3 papers with code
A DNN based Normalized Time-frequency Weighted Criterion for Robust Wideband DoA Estimation
However, the best-performing design of T-F weights is criterion-dependent in general.
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexities of any CPWL function are at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.
A Generalized Proportionate-Type Normalized Subband Adaptive Filter
Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems.
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees
To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i. e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets.
Speech Recovery for Real-World Self-powered Intermittent Devices
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications.
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.
