home | Download | Guestbook | Sitemap
codelookerDownloadNumerical Algorithm-Artificial Intelligencematlab
Search:
An-Introduction-to-Numerical-and-Analytical-Metho
  • Classification:Numerical Algorithm-Artificial Intelligence - matlab
  • Development Tool:matlab
  • Sise:205 KB
  • Upload time:2014/5/24 16:05:16
  • Uploader:syntax
  • Download Statistics:
Description
adv numerical and analytical methods




File list:
9781466576025_examples
.....................\Chapter 10
.....................\..........\Example_10_6.m
.....................\..........\Figure_10_11.mdl
.....................\..........\Figure_10_13.mdl
.....................\..........\Figure_10_14.mdl
.....................\..........\Figure_10_16.mdl
.....................\..........\Figure_10_17.mdl
.....................\..........\Figure_10_18.mdl
.....................\..........\Figure_10_19.mdl
.....................\..........\Figure_10_20.mdl
.....................\..........\Figure_10_21.mdl
.....................\..........\Figure_10_23.mdl
.....................\..........\Figure_10_25.mdl
.....................\..........\Figure_10_26.mdl
.....................\..........\Figure_10_27.mdl
.....................\..........\Figure_10_31.mdl
.....................\..........\Figure_10_5.mdl
.....................\..........\Figure_10_6.mdl
.....................\..........\Figure_10_7.mdl
.....................\..........\Figure_10_8.mdl
.....................\..........\Figure_10_9.mdl
.....................\Chapter 11
.....................\..........\confun_silo.m
.....................\..........\confun_silo2.m
.....................\..........\Example_11_1.m
.....................\..........\Example_11_10.m
.....................\..........\Example_11_11.m
.....................\..........\Example_11_2.m
.....................\..........\Example_11_3.m
.....................\..........\Example_11_4.m
.....................\..........\Example_11_5.m
.....................\..........\Example_11_6.m
.....................\..........\Example_11_8.m
.....................\..........\Example_11_9.m
.....................\Chapter 12
.....................\..........\Example_12_1.m
.....................\..........\Example_12_3.m
.....................\Chapter 14
.....................\..........\dYdt_laplace.m
.....................\..........\Example_14_2.m
.....................\..........\Example_14_3.m
.....................\..........\Example_14_5.m
.....................\Chapter 2
.....................\.........\atm_properties.txt
.....................\.........\Example_2_1.m
.....................\.........\Example_2_10.m
.....................\.........\Example_2_11.m
.....................\.........\Example_2_11b.m
.....................\.........\Example_2_12.m
.....................\.........\Example_2_13.m
.....................\.........\Example_2_14.m
.....................\.........\Example_2_15.m
.....................\.........\Example_2_16.m
.....................\.........\Example_2_2.m
.....................\.........\Example_2_3.m
.....................\.........\Example_2_4.m
.....................\.........\Example_2_5.m
.....................\.........\Example_2_6.m
.....................\.........\Example_2_7.m
.....................\.........\Example_2_8.m
.....................\.........\Example_2_9.m
.....................\.........\for_loop_assignment.m
.....................\Chapter 3
.....................\.........\atm_properties.txt
.....................\.........\Example_3_1.m
.....................\.........\Example_3_10.m
.....................\.........\Example_3_11.m
.....................\.........\Example_3_12.m
.....................\.........\Example_3_13.m
.....................\.........\Example_3_14.m
.....................\.........\Example_3_2.m
.....................\.........\Example_3_3.m
.....................\.........\Example_3_5.m
.....................\.........\Example_3_6.m
.....................\.........\Example_3_7.m
.....................\.........\Example_3_8.m
.....................\.........\Example_3_9.m
.....................\.........\exf1.m
.....................\.........\exf2.m
.....................\.........\func_grade.m
.....................\Chapter 4
.....................\.........\Example_4_1.m
.....................\.........\Example_4_10.m
.....................\.........\Example_4_2.m
.....................\.........\Example_4_3.m
.....................\.........\Example_4_4.m
.....................\.........\Example_4_5.m
.....................\.........\Example_4_9.m
.....................\Chapter 5
.....................\.........\Example_5_1.m
.....................\.........\Example_5_2.m
.....................\.........\Example_5_3.m
.....................\.........\func_RLC.m
.....................\Chapter 6
.....................\.........\Example_6_1.m
.....................\.........\Example_6_2.m
.....................\.........\Example_6_3.m
.....................\.........\Example_6_4.m
.....................\.........\Example_6_5.m
.....................\.........\Example_6_6.m
.....................\.........\Example_6_7.m
.....................\.........\hemisphere_V.m
.....................\Chapter 7
.....................\.........\dydt3.m
.....................\.........\Example_7_1.m
.....................\.........\Example_7_2.m
.....................\.........\Example_7_3.m
.....................\.........\Example_7_4.m
.....................\.........\Example_7_5.m
.....................\.........\Example_7_6.m
.....................\Chapter 8
.....................\.........\Example_8_1.m
.....................\.........\Example_8_2.m
.....................\.........\Example_8_3.m
.....................\Chapter 9
.....................\.........\awake3.txt
.....................\.........\Example_9_1.m
.....................\.........\Example_9_2.m
.....................\.........\Example_9_3.m
.....................\.........\Example_9_4.m
.....................\.........\Example_9_5.m
.....................\README.txt
Related source code
[Approximate-Bayesian-Inference-for-R...] - Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. Theseparameters must often be estimated from noisy observations since speech signals arerarely obtained in ‘clean’ acoustic environments in the real world. As a result, theparameter estimation algorithms we employ must be robust to environmental factorssuch as additive noise and reverberation. In this work we derive and evaluate approximate Bayesian algorithms for the following speech processing tasks: 1) speechenhancement 2) speaker identification 3) speaker verification and 4) voice activitydetection.
Download Address
download DownLoad
Comments: Don't forget to comment after downloading! Comment...
About - Advertise - Sitemap