文件名称:DNN_toolbox
- 所属分类:
- 人工智能/神经网络/遗传算法
- 资源属性:
- [Matlab] [源码]
- 上传时间:
- 2016-08-17
- 文件大小:
- 76.2mb
- 下载次数:
- 0次
- 提 供 者:
- 王*
- 相关连接:
- 无
- 下载说明:
- 别用迅雷下载,失败请重下,重下不扣分!
介绍说明--下载内容均来自于网络,请自行研究使用
语音分离用的深度神经网络工具箱,matlab,非常全
-This folder contains Matlab programs for a toolbox for supervised speech separation using deep neural networks (DNNs).
-This folder contains Matlab programs for a toolbox for supervised speech separation using deep neural networks (DNNs).
(系统自动生成,下载前可以参看下载内容)
下载文件列表
DNN_toolbox
...........\config
...........\......\list.txt
...........\......\list120.txt
...........\......\list120_short.txt
...........\......\list600.txt
...........\......\list600_short.txt
...........\DATA
...........\dnn
...........\...\main
...........\...\....\checkPerformanceOnData_no_print.m
...........\...\....\checkPerformanceOnData_no_print_wiener.m
...........\...\....\checkPerformanceOnData_save_IBM.m
...........\...\....\checkPerformanceOnData_save_wiener.m
...........\...\....\computeNetGradientNoRolling.m
...........\...\....\dnn_train.m
...........\...\....\forwardPass.m
...........\...\....\forwardPass_diff_drop_ratio.m
...........\...\....\funcDeepNetTrainNoRolling.m
...........\...\....\getOutputFromNet.m
...........\...\....\getOutputFromNetSplit.m
...........\...\....\stoi.m
...........\...\mvn_store.m
...........\...\pretraining
...........\...\...........\pretrainRBMStack.m
...........\...\...........\trainRBM.m
...........\...\run_every.m
...........\...\utility
...........\...\.......\batchComputeMeanStd.m
...........\...\.......\compute_unit_activation.m
...........\...\.......\compute_unit_gradient.m
...........\...\.......\count_struct.m
...........\...\.......\deltas.m
...........\...\.......\format_print.m
...........\...\.......\gather_net.m
...........\...\.......\genBatchID.m
...........\...\.......\getHITFA.m
...........\...\.......\getMSE.m
...........\...\.......\getNetParamStr.m
...........\...\.......\initializeRandWSparse.m
...........\...\.......\initRandW.m
...........\...\.......\initRandWSparse.m
...........\...\.......\make_labels.m
...........\...\.......\make_window_buffer.m
...........\...\.......\meanVarArmaNormalize_Test.m
...........\...\.......\meanVarNormalize.m
...........\...\.......\meanVarNormalize_Test.m
...........\...\.......\mean_var_norm.m
...........\...\.......\mean_var_norm_testing.m
...........\...\.......\netRolling.m
...........\...\.......\netUnRolling.m
...........\...\.......\randInitNet.m
...........\...\.......\randinitWbSparse.m
...........\...\.......\relu.m
...........\...\.......\relu_grad.m
...........\...\.......\resyn
...........\...\.......\.....\cochleagram.m
...........\...\.......\.....\cochplot.m
...........\...\.......\.....\erb2hz.m
...........\...\.......\.....\f_af_bf_cf.mat
...........\...\.......\.....\gammatone.m
...........\...\.......\.....\hz2erb.m
...........\...\.......\.....\ibm.m
...........\...\.......\.....\loudness.m
...........\...\.......\.....\meddis.m
...........\...\.......\.....\synthesis.m
...........\...\.......\sigmoid.m
...........\...\.......\sigmoid_grad.m
...........\...\.......\softmax.m
...........\...\.......\unroll_struct.m
...........\...\.......\zeroInitNet.m
...........\gen_mixture
...........\...........\generate_test_mix.m
...........\...........\generate_train_mix.m
...........\...........\get_all_noise_test.m
...........\...........\get_all_noise_train.m
...........\get_feat
...........\........\features
...........\........\........\ams
...........\........\........\...\AMS_init_FFT.m
...........\........\........\...\create_crit_filter.m
...........\........\........\...\env_extraction_gmt_chan2.m
...........\........\........\...\extract_AMS_perChan.m
...........\........\........\...\get_amsfeature_chan_fast.m
...........\........\........\...\mel.m
...........\........\........\cochleagram
...........\........\........\...........\cochleagram.m
...........\........\........\...........\erb2hz.m
...........\........\........\...........\f_af_bf_cf.mat
...........\........\........\...........\gammatone.m
...........\........\........\...........\hz2erb.m
...........\........\........\...........\ibm.m
...........\........\........\...........\ideal.m
...........\........\........\...........\loudness.m
...........\........\........\...........\meddis.m
...........\........\........\...........\synthesis.m
...........\........\........\...........\wiener.m
...........\........\........\my_features_AmsRastaplpMfccGf.m
...........\........\........\rastamat
...........\........\........\..