如何将mfcc矢量与注释中的标签结合起来传递给神经网络(How to combine mfcc vector with labels from annotation to pass to a neural network)

 使用librosa，我为我的音频文件创建了mfcc，如下所示： 
 
import librosa
y, sr = librosa.load('myfile.wav')
print y
print sr
mfcc=librosa.feature.mfcc(y=y, sr=sr)
 
 我还有一个文本文件，其中包含与音频对应的手动注释[start，stop，tag]，如下所示： 
 
 
 
 0.0 2.0声音1 
 2.0 4.0 sound2 
 4.0 6.0沉默 
 6.0 8.0 sound1 
 
 
 问题：如何将生成的librosa生成的mfcc与文本文件中的注释结合起来。 
 
 最终目标是，我想结合对应于标签的mfcc，并传递 
 它到神经网络。 
 因此，神经网络将mfcc和相应的标签作为训练数据。 
 
 如果它是一维的，我可以有N列N值，最后一列Y带有Class标签。 但我很困惑如何继续，因为mfcc的形状类似于（16，X）或（20，Y）。 所以我不知道如何将两者结合起来。 
 
 我的样本mfcc在这里： https ： //gist.github.com/manbharae/0a53f8dfef6055feef1d8912044e1418 
 
 请帮忙谢谢。 
 
 更新：目标是训练神经网络，以便在将来遇到它时识别出新的声音。 
 
 我用Google搜索，发现mfcc非常适合演讲。 但是我的音频有语音，但我想识别非语音。 是否有其他推荐的音频功能用于通用音频分类/识别任务？ 

Using librosa, I created mfcc for my audio file as follows:
 
import librosa
y, sr = librosa.load('myfile.wav')
print y
print sr
mfcc=librosa.feature.mfcc(y=y, sr=sr)
 
I also have a text file that contains manual annotations[start, stop, tag] corresponding to the audio as follows: 
 
 
 
0.0 2.0 sound1
 2.0 4.0 sound2
 4.0 6.0 silence
 6.0 8.0 sound1
 
 
QUESTION: How to do I combine the generated mfcc's that was generated by librosa, with the annotations from text file.
 
Final goal is, I want to combine mfcc corresponding to the label, and pass
 it to a neural network.
 So a neural network will have the mfcc and corresponding label as training data. 
 
If it was one dimensional , I could have N columns with N values and the final Column Y with a Class label. But i'm confused how to proceed, as the mfcc has the shape of something like (16, X) or (20, Y). So I don't know how to combine the two.
 
My sample mfcc's are here : https://gist.github.com/manbharae/0a53f8dfef6055feef1d8912044e1418
 
Please help thank you. 
 
Update : Objective is to train a neural network so that it can identify a new sound when it encounters it in the future. 
 
I googled and found that mfcc are very good for speech. However my audio has speech but I want to indentify non speech. Are there any other recommended audio features for a general purpose audio classification/recognition task?

原文：https://stackoverflow.com/questions/48388641