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vad's Introduction

v0: 2017年暑假用于Human Activity Detection的版本,最初版。

特征为cmn后的 MFCC + delta1 + delta2
label读取采用Textgrid
通过feats_io.py存储feats和labels文件,格式为:*.idx(索引),*.ark(数据)
卷积神经网络,网络返回值:2分类,取大者为output label

v1: 在v0的基础上添加如下修改

从segment中读取label
网络最后的输出层添加sigmoid函数,单值输出,表示该帧为语音帧的概率

v2:在v0的基础上增加大幅度的修改

通过prepare_data.py提取特征为±40帧的频谱,合并保存为*.npy文件。
卷积神经网络,只保留需要的模型,其他删除。网络返回值:2分类。
网络存在待优化问题,本Version需要改进,如果表现不如v1可以考虑删除

v3:基于SCD任务的LSTM版本更改

修改标签计算和网络模型

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