Tag Archives: data

code

Plot two parameters for categories

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This is an examle how to plot values for two parameters in on plot and builds upon the dta generated at this example.
So, from the features you extracted you would isolate two parameters from the dataframe:

x1 = df_feats.loc[:, 'F0semitoneFrom27.5Hz_sma3nz_amean']
x2 = df_feats.loc[:, 'F0semitoneFrom27.5Hz_sma3nz_stddevNorm']

You'd need matplotlib

import matplotlib.pyplot as plt

You would color the dots according to the emotion they have been labeled with. Because the plot function does not accept string values as color designators but only numbers, you'd first have to convert them, e.g. with the LabelEncoder:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
c_vals = le.fit_transform(df_emo.emotion.values)

and then you can simply do the plot:

plt.scatter(x1, x2, c=c_vals)
plt.show()

tutorial

Use speechalyzer to walk through a large set of audio files

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I wrote speechalyzer in Java to process a large set of audio files. Here's how you could use this on your audio set.

Install and configure

1) Get it and put it somewhere on your file system, don't forget to also install its GUI, the Labeltool
2) Make sure you got Java on your system.
3) Configure both programs by editing the resource files.

Run

The easiest case is if all of your files are in one directory. You would simply start the Speechalyzer like so (you need to be in the same directory):

java - jar Speechalyzer.jar -rd <path to folder with audio files> &

make sure you configured the right audio extension and sampling rate in the config file (wav format, 16kHz is default).
Then change to the Labeltool directory and start it simply like this:

java - jar Labeltool.jar &

again you might have to adapt the sample rate in the config file (or set it in the GUI). Note you need to be inside the Labeltool directory. Here is a screenshot of the Labeltool displaying some files which can be annotated, labeled or simply played in a chain: