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[29535] Artykuł:

Parallel Processing of Statistical Data Using Graphical Processor for Changeability Evaluation of Human Voice Parameters

Czasopismo: Proceedings of the International Conference Mixed Design of Integrated Circuits and Systems   Strony: 643-646
Opublikowano: 2010
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Procent
udziału		 Liczba
punktów
Damian Krzesimowski orcid logoPolitechnika Łódzka25.00  
M. WojteraPolitechnika Łódzka25.00  
Zygmunt CiotaPolitechnika Łódzka25.00  
B. SakowiczPolitechnika Łódzka25.00  

Grupa MNiSW:  Pozostałe publikacje (niepunktowane)
Punkty MNiSW: 0

Web of Science LogoYADDA/CEON    
Keywords:

statistical analysis  computer graphic equipment  coprocessors  instruction sets  parallel processing  speech processing  graphic chips manufacturers  parallel processing  statistical data  graphical processor  changeability evaluation  human voice parameters  memory consuming process  signal processing operations  multicore architecture  single instruction set  Kernel  Graphics processing unit  MATLAB  Signal processing  Polynomials  Root mean square  MEX plugins  parallel processing  signal pr 


Abstract:

Advanced computer analysis of human voice is still time and memory consuming process. One of the greatest problems is reduction of time calculations to obtain the proper results, even for relatively short samples of the voice. All signal processing operations require significant number of calculations, therefore we propose to exploit parallel processing capabilities of graphical processor. Due to requirements of graphics generation, graphical processing units have become specialized chipsets with multi-core architecture, capable of executing a single instruction set on multiple sets of data. This approach is desirable in data intensive calculations, therefore special libraries were made by graphic chips manufacturers, to enable an use of these chips in non-graphical applications. It is demonstrated in this paper that using graphical processors for signal processing, results can be obtained in a shorter time, feasible for processing of multiple full length samples.


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