A systematic review of compressive sensing: Concepts, implementations and applications
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
Trends in compressive sensing for EEG signal processing applications
The tremendous progress of big data acquisition and processing in the field of neural
engineering has enabled a better understanding of the patient's brain disorders with their …
engineering has enabled a better understanding of the patient's brain disorders with their …
Sparse linear prediction and its applications to speech processing
The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech
processing tools created by introducing sparsity constraints into the linear prediction …
processing tools created by introducing sparsity constraints into the linear prediction …
Compressive sensing: Methods, techniques, and applications
According to the latest research, it is very much clear that in future we require a huge amount
of data as modern advancement in communication and signal processing generates a large …
of data as modern advancement in communication and signal processing generates a large …
Integrated detection and Imaging algorithm for radar sparse targets via CFAR-ADMM
Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried
out in-norm regularization and considers that the SAR image contains only targets and …
out in-norm regularization and considers that the SAR image contains only targets and …
Cognitive speech coding: examining the impact of cognitive speech processing on speech compression
Speech coding is a field in which compression paradigms have not changed in the last 30
years. Speech signals are most commonly encoded with compression methods that have …
years. Speech signals are most commonly encoded with compression methods that have …
Voiced/nonvoiced detection in compressively sensed speech signals
We leverage the recent algorithmic advances in compressive sensing (CS), and propose a
novel unsupervised voiced/nonvoiced (V/NV) detection method for compressively sensed …
novel unsupervised voiced/nonvoiced (V/NV) detection method for compressively sensed …
Sparse coding based features for speech units classification
In this work, we propose sparse representation based features for speech units classification
tasks. In order to effectively capture the variations in a speech unit, the proposed method …
tasks. In order to effectively capture the variations in a speech unit, the proposed method …
Greedy double sparse dictionary learning for sparse representation of speech signals
This paper proposes a greedy double sparse (DS) dictionary learning algorithm for speech
signals, where the dictionary is the product of a predefined base dictionary, and a sparse …
signals, where the dictionary is the product of a predefined base dictionary, and a sparse …
Compressive sensing framework for speech signal synthesis using a hybrid dictionary
Compressive sensing (CS) is a promising focus in signal processing field, which offers a
novel view of simultaneous compression and sampling. In this framework a sparse …
novel view of simultaneous compression and sampling. In this framework a sparse …