Tag Archives: opensmile

How to compare formant tracks extracted with opensmile vs. Praat

Note to install not parselmouth but the package praat-parselmouth:

!pip install praat-parselmouth

First, some imports

import pandas as pd
import parselmouth 
from parselmouth import praat
import opensmile
import audiofile

Then, a test file:

testfile = '/home/felix/data/data/audio/testsatz.wav'
signal, sampling_rate = audiofile.read(testfile)
print('length in seconds: {}'.format(len(signal)/sampling_rate))

Get the opensmile formant tracks by copying them from the official GeMAPS config file

smile = opensmile.Smile(
result_df = smile.process_file(testfile)
centerformantfreqs = ['F1frequency_sma3nz', 'F2frequency_sma3nz', 'F3frequency_sma3nz']
formant_df = result_df[centerformantfreqs]

Get the Praat tracks (smile configuration computes every 10 msec with frame length 20 msec)

sound = parselmouth.Sound(testfile) 
formants = praat.call(sound, "To Formant (burg)", 0.01, 4, 5000, 0.02, 50)
f1_list = []
f2_list = []
f3_list = []
for i in range(2, formants.get_number_of_frames()+1):
    f1 = formants.get_value_at_time(1, formants.get_time_step()*i)
    f2 = formants.get_value_at_time(2, formants.get_time_step()*i)
    f3 = formants.get_value_at_time(3, formants.get_time_step()*i)

To be sure: compare the size of the output:

print('{}, {}'.format(result_df.shape[0], len(f1_list)))

combine and inspect the result:

formant_df['F1_praat'] = f1_list
formant_df['F2_praat'] = f2_list
formant_df['F3_praat'] = f3_list

How to extract formant center frequencies (or other acoustic features) from speech data with opensmile in python

There is a framework called OpenSMILE published on Github that can be used to extract high level acoustic features from audio signals and I’d like to show you how to use it with Python.

I’ve set up a notebook for this here.

First you need to install opensmile.

pip install opensmile

General procedure

There are two ways to extract a specific acoustic feature with opensmile:

1) Use an existing config that contains your target feature and filter it from the results
2) Write your own config file and extract only your target feature directly

Method 1 is easier but obviously not resource efficient, 2 is better but then to learn the opensmile config syntax and all the existing modules is not trivial.

Using one example for an acoustic feature: formants, we’ll do both ways. The documentation for the python wrapper of opensmile is here

The following assumes you got a test wave file recorded and stored somewhere:

testfp = '/kaggle/input/testdata/testsatz.wav'

Method 1): Use an existing config file that includes the first three formant frequencies

We start with instantiating the main extractor class, Smile, with a configuration that includes formants. The GeMAPSv01b features set has been derived from the GeMAPS feature set

smile = opensmile.Smile(

Extract this for our test sentence, out comes a pandas dataframe

result_df = smile.process_file(testfp)

Now use only the three center formant frequencies

centerformantfreqs = [‘F1frequency_sma3nz’, ‘F2frequency_sma3nz’, ‘F3frequency_sma3nz’]
formant_df = result_df[centerformantfreqs]

This should be your ouput: per frame three values: the center frequencies of the formants:


Method 2): Write your own config file

The documentation for opensmile config files is here.
Most often it is probably easier to look at an existing config file and copy/paste the components you need.

You could edit the opensmile config in a string:

formant_conf_str = '''

;;; default source

;;; source

\{\cm[source{?}:include external source]}

;;; main section

instance[framer].type = cFramer
instance[win].type = cWindower
instance[fft].type = cTransformFFT
instance[resamp].type = cSpecResample
instance[lpc].type = cLpc
instance[formant].type = cFormantLpc

reader.dmLevel = wave
writer.dmLevel = frames
copyInputName = 1
frameMode = fixed
frameSize = 0.025000
frameStep = 0.010000
frameCenterSpecial = left
noPostEOIprocessing = 1



targetFs = 11000



;;; sink

\{\cm[sink{?}:include external sink]}

which you can save as a config file:

with open('formant.conf', 'w') as fp:

Now we reinstantiate our smile object with the custom config

smile = opensmile.Smile(

and extract again

formant_df_2 = smile.process_file(testfp)

Voila! The output is should be similar to the one you got with the first method.

Machine classification of emotional speech with EmoDB and python

This is a tutorial on how to

  • configure a python environment with Jupyter notebook
  • download Berlin EmoDB
  • import the audformat database
  • extract acoustic features with opensmile
  • perform a machine classification with sklearn

It does expect some experience with

  • unix commands
  • python
  • pandas

So if you miss this you might have to google the stuff you don't understand.
In case you know German and use Windows I recorded this screencast for you.

There is a Kaggle notebook that you could use to try this out.

Configure a python environment

I start from the point where you got python installed on your machine and have a shell (console window).
I use Unix commands here, most of them should also work on Mac OS, for Windows you might have to adapt some (e.g. 'ls' becomes 'dir').

So if you type 


in your shell, the python interpreter should start, you can quit it with the command 


Create a subfolder for your project and enter it, e.g.

mkdir emodb; cd emodb

Create a virtual environment for your project

python3 -m venv ./venv 

Activate your project

source ./venv/bin/activate

which should result in your prompt including the environment name like e.g. this


You can leave your environment with the


command. For now though, please make sure you have the environment activated. You can then install the most important packages with pip like this:

pip install pandas numpy jupyter audformat opensmile sklearn matplotlib

If all goes well, you should now be able to start up the jupyter server which should give you an interface in your browser window.

jupyter notebook &

And create a new notebook by clicking the "New" button near the left top corner.

Get and unpack the Berlin Emodb emotional database

You would start by downloading and unpacking emodb like this (of course you can do this as well outside the notebook in your shell):

!wget -c https://tubcloud.tu-berlin.de/s/8Td8kf8NXpD9aKM/download
!mv download emodb_audformat.zip
!unzip emodb_audformat.zip
!rm emodb_audformat.zip


import audformat
db = audformat.Database.load('./emodb')

you can load the database and inspect the contents.

You still have to state the absolute path to the audio files for further processing. You would find the current directory with the


command, and would add the emodb folder name to it and prefix this to the wav file paths like so

import os
root = '/...my current directory.../emodb/'
db.map_files(lambda x: os.path.join(root, x))

To check that this worked you might want to listen to a sample file

import IPython

which should give you a GUi like this

Extract acoustic features

EmoDB is annotated with emotional labels. If we want to classify these emotions automatically we need to extract acoustic features first.

We can do this easily in python with dedicated packages for this like the Praat software or opensmile. In this tutorial we'll use opensmile.

First we will get the Pandas dataframes from the database like this:

df_emo = db.tables['emotion'].df
df_files = db.tables['files'].df

and might want to inspect the class distribution with pandas


then, with

import opensmile
smile = opensmile.Smile(

you construct your feature extractor and with

df_feats = smile.process_files(df_emo.index)

should be able to extract the 62 GeMAPS acoustic features, which you could check by looking at the dimension of the dataframe


and looking at the first entry


You might run into trouble later because the smile.process function per default results in a multiindex with filename, start and end time (because you might have extracted low level features per frame). In the following picture i show my screen so far to illustrate the situation.

So you end up with three data frames:

  • df_emo with emotion labels and confidence
  • df_files with duration, speaker id and transcript index
  • df_feats with the features
    So in order to be able to match the indeces from this three dataframes, I dropped the start and end columns from the features dataframe with the following lines:

    df_feats.index = df_feats.index.droplevel(1) # drop start level
    df_feats.index = df_feats.index.droplevel(1) # drop end level

Perform a statistical classification on the data

Now we would conclude this tutorial by performing a first machine classification.
You basically need four sets of data for this: each a feature and label set for a training and a test (or better: development) set.

In a naive approach, we use the first 100 entries of the EmoDB for test and the others for training:

test_labels = df_emo.iloc[:100,].emotion
train_labels = df_emo.iloc[100:,].emotion
test_feats = df_feats.iloc[:100,]
train_feats =  df_feats.iloc[100:,]

There are numerous possibilities to use machine classifiers in python, if we don't want to code one ourselves we might want to use on from the sklearn package, for example an implementation of the SVM (support vector machine) algorithm

from sklearn import svm
clf = svm.SVC()

train it with our training features and labels

clf.fit(train_feats, train_labels)

, compute predictions on the test features

pred_labels  = clf.predict(test_feats)

and compare the predictions with the real labels (aka ''ground truth'') with a confusion matrix

from sklearn.metrics import confusion_matrix
confusion_matrix(test_labels.emotion, pred_labels)

and by computing the unweighted average recall (UAR)

from sklearn.metrics import recall_score
recall_score(test_labels, pred_labels, average='macro')

This results in chance level as the SVM classifier lazily always decided on the majority class. The results can be improved to something more meaningful, e.g. by passing better meta parameters when constructing the classifier:

clf = svm.SVC(kernel='linear', C=.001)

and repeating the experiment, which should result in a confusion matrix like this one (see Kaggle notebook for code):


This concludes the tutorial so far, what to do next?

Here are some suggestions:

  • What is really problematic with the above approach is that the training and the test set are not speaker independent, i.e. the same 10 speakers appear in both sets.
    • Which means you can not know if the classifier learned anything about emotions or (more probable) some idiosyncratic peculiarities of the speakers.
    • With so few speakers it doesn't make a lot of sense to further divide them, so what people often do is perform a LOSO (leave-one-speaker-out) or do x-cross validation by testing x times a part of the speakers against the others (in the case of EmoDB this would be the same if x=10).
  • What's also problematic is that you only looked at one (very small, highly artificial) database and this usually does not result in a usable model for human emotional behavior.
    • Try to import a different database or record your own, map the emotions to the EmoDB set and see how this performs.
  • SVMs are great, but you might want to try other classifiers.
    • Perform a grid search on the best meta-parameters for the SVM.
    • Try other sklearn classifiers.
    • Try other famous classifiers like e.g. XGBoost.
    • Try ANNs (artificial neural nets) with keras or torch.
  • Try other features
    • There are other opensmile feature set configurations available.
    • Do feature selection and to identify the best ones to see if they make sense.
    • Try other features, e.g. from Praat or other packages.
    • Try embeddings from pretrained ANNs like e.g. Trill or PANN features.
  • The opensmile features are all given as absolute values.
    • Try to normalize them with respect to the training set or each speaker individually.
  • Generalization is often improved by adding acoustic conditions to the training:
    • Try augmenting the data by adding samples mixed with noise or bandpass filters.
  • Last not least: code an interface that lets you test the classifier on the spot.