Simple Audio Analysis with pyAudioAnalysis

I had a need to do some classification of sets of environmental audio files so started looking for an easy to use Python library that was up for the task. Here are my notes on setting up the pyAudioAnalysis Library for simple audio classification task.


This library relies on Python 2, so do a quick python –version to make sure you are not on Python 3. If both are installed on your machine they may be accessible as python and python3


You will need to make sure the following modules are installed:

  • numpy
  • matplotlib
  • scipy
  • gsl
  • mlpy
  • scikit-learn (v 0.16.1)
  • Simplejson

Note that the Anaconda Python 2.7 distribution has many of these packages already included, but you need scikit-learn (v 0.16.1) and the latest Anaconda distribution delivers the 0.17.1 version. If you get an error message referencing a missing hmm component, you likely have the 0.17.* version of scikit-learn, as hmm was deprecated in that version.

When the dependencies are all verified or installed, simply clone the repo into a directory you want to use for development.

mkdir ~/audioClassification
cd audioClassification
mkdir trainingData
mkdir sampleData
git clone

Training Data

The simplest way to get started it to create sub directories under trainingData for each sound category you wish to recognize. For this example lets use these

mkdir trainingData/breaking_glass 
mkdir trainingData/bubbles 
mkdir trainingData/vacuum

Next do a Google search looking for free audio files to add to the trainingData directory: e.g. breaking glass audio wav free, then for bubble sounds try bubbles audio wav free, finally for vacuum sounds try vacuum audio wav free. Save a dozen or so examples of each sound into the corresponding sub-directory under trainingData. The pyAudioAnalysis library requires wav files, so make sure any files you save to trainingData are wav files.

Sample Data

Next add some audio samples that can be used to test the training. The search is the same as above, but just choose different sample files, so you can test how well the classification model works. You can save them directly under the sampleData directory for this example. It is also good to choose a few random other sounds to see how they are interpreted by the classification model.


I wrote two quick scripts to do the classification and test a file against the classification model.

createClassifierModel Script

From inside the audioClassification directory

touch createClassifierModel
chmod +x createClassifierModel

In your favorite editor open createClassifierModel and add the following code:

from pyAudioAnalysis import audioTrainTest as aT
import os
from sys import argv
script, dirname = argv

subdirectories = os.listdir(dirname)[:4]

subdirectories = [dirname + "/" + subDirName for subDirName in subdirectories]

aT.featureAndTrain(subdirectories, 1.0, 1.0, aT.shortTermWindow, aT.shortTermStep, "svm", "svmModel", False)

testClassifierModel Script

From inside the audioClassification directory

touch testClassifierModel
chmod +x testClassifierModel

In your favorite editor open testClassifierModel and add the following code:

from sys import argv
import numpy as np
from pyAudioAnalysis import audioTrainTest as aT
script, filename = argv
isSignificant = 0.8 #try different values.

# P: list of probabilities
Result, P, classNames = aT.fileClassification(filename, "svmModel", "svm")
winner = np.argmax(P) #pick the result with the highest probability value.

# is the highest value found above the isSignificant threshhold? 
if P[winner] > isSignificant :
  print("File: " +filename + " is in category: " + classNames[winner] + ", with probability: " + str(P[winner]))
else :
  print("Can't classify sound: " + str(P))

Run the createClassifierModel Script

To create a classification model simply run the createClassifierMode script and pass in the name of the training directory as an argument to the script.

createClassifierModel trainingData

Below is an example output of the script.

                breaking_glass                  bubbles                 vacuum          OVERALL
        C       PRE     REC     F1      PRE     REC     F1      PRE     REC     F1      ACC     F1
        0.001   58.1    100.0   73.5    100.0   56.0    71.8    33.3    0.0     0.0     64.0    48.4
        0.010   83.7    100.0   91.1    100.0   95.0    97.4    98.5    65.0    78.3    90.0    89.0     best F1         best Acc
        0.500   80.0    94.0    86.4    100.0   99.0    99.5    81.8    54.0    65.1    85.2    83.7
        1.000   79.8    98.5    88.1    100.0   91.0    95.3    91.9    57.0    70.4    86.2    84.6
        5.000   79.4    94.5    86.3    100.0   93.0    96.4    81.2    56.0    66.3    84.5    83.0
        10.000  77.7    97.5    86.5    100.0   89.0    94.2    90.0    54.0    67.5    84.5    82.7
Confusion Matrix:
        bre     bub     vac
bre     50.0    0.0     0.0
bub     1.0     23.7    0.3
vac     8.8     0.0     16.2
Selected params: 0.01000

Run the testClassifierModel Script

To categorize an audio file simply pass the file name as an argument to the testClassifierModel script. In the example below a sample was named bubbles1.wav

testClassifierModel sampleData/bubbles1.wav

Below is an example output of the script.

File: sampleData/bubbles1.wav is in category: bubbles, with probability: 0.95486693161

Potential GOTCHAS

One issue is that not all wav files will be understood by the library. If the classification hangs or aborts then it is possible one of the downloaded wav files is the issue. I found the simple solution was to add a line to that printed the filename currently being worked on so I could see what was happening the first time using a new set of training files.

for me this is the location

datadef read(filename, mmap=False):
    Return the sample rate (in samples/sec) and data from a WAV file

    filename : string or open file handle
        Input wav file.
    mmap : bool, optional
        Whether to read data as memory mapped.
        Only to be used on real files (Default: False)

        .. versionadded:: 0.12.0

    rate : int
        Sample rate of wav file
    data : numpy array
        Data read from wav file

    * The file can be an open file or a filename.
    * The returned sample rate is a Python integer.
    * The data is returned as a numpy array with a data-type determined
      from the file.
    * This function cannot read wav files with 24 bit data.

    print("FILENAME: " + filename)


This was a basic intro to help you get started with pyAudioAnalysis library, check out the project wiki page for more use case for the library including other classification model options, as well as feature extraction, segmentation and visualization capabilities.