Title :
Gradient-descent based window optimization for linear prediction analysis
Author_Institution :
Mobile Media Lab., DoCoMo USA Labs, San Jose, CA, USA
Abstract :
The autocorrelation method of linear prediction (LP) analysis relies on a window for data extraction; we propose an approach to optimize the window based on gradient-descent. It is shown that the optimized window has improved performance with respect to popular windows, such as Hamming. The technique has potential in quality improvement for many LP-based speech coders.
Keywords :
correlation methods; data compression; gradient methods; optimisation; prediction theory; speech coding; Hamming window; LP-based speech coders; autocorrelation method; data extraction; gradient-descent based window optimization; linear prediction analysis; optimized window; signal windowing; speech coding algorithms; Autocorrelation; Data mining; Equations; Laboratories; Optimization methods; Performance gain; Signal synthesis; Speech analysis; Speech coding; Speech synthesis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1198817
