4. Other Yaafe tools

4.1. Matlab interaction

If you enabled the WITH_MATLAB_MEX option, you can extract features using the Yaafe matlab class.

The Matlab Yaafe engine needs to be configured with a dataflow file. Once you get the dataflow file corresponding to the features to extract and the analysis sample rate, you can prepare the Matlab Yaafe engine:

>> yaafe = Yaafe();
>> yaafe.load('yaflow')
1

‘yaflow’ is the Dataflow file. the ‘load’ method returns 1 if success or 0 if failed.

Then, the Matlab Yaafe is ready to extract features:

>> signal = rand(1,100000);
>> feats1 = yaafe.process(signal);
>> feats2 = yaafe.processFile('song.wav');
>> feats1

feats1 =

      mfcc: [1x1 struct]
   mfcc_d1: [1x1 struct]
   mfcc_d2: [1x1 struct]
        sf: [1x1 struct]
        sr: [1x1 struct]

Once Dataflow file is loaded, you can call the ‘process’ and ‘processFile’ methods as many times as you want. The output is a struct where each fields holds an audio features with the following metadata:

name:the feature name has defined in the Dataflow file size:size of the feature sampleRate:analysis sample rate sampleStep:number of sample between consecutive analysis windows frameLength:length of analysis window data:feature values

Note

If you prefer to manipulate a feature list, you can use the struct2cell Matlab function:

>> feats = struct2cell(feats);
>> feats

feats =

    [1x1 struct]
    [1x1 struct]
    [1x1 struct]
    [1x1 struct]
    [1x1 struct]

4.2. Python interaction

Yaafe python bindings allow to easily extract features from Python with a great flexibility. The first step is always to build the DataFlow object corresponding to the audio features to extract (for example using a FeaturePlan object), and configure an Engine.

>>> from yaafelib import *
>>>
>>> # Build a DataFlow object using FeaturePlan
>>> fp = FeaturePlan(sample_rate=16000)
>>> fp.addFeature('mfcc: MFCC blockSize=512 stepSize=256')
True
>>> fp.addFeature('mfcc_d1: MFCC blockSize=512 stepSize=256 > Derivate DOrder=1')
True
>>> fp.addFeature('mfcc_d2: MFCC blockSize=512 stepSize=256 > Derivate DOrder=2')
True
>>> df = fp.getDataFlow()
>>>
>>> # or load a DataFlow from dataflow file.
>>> df = DataFlow()
>>> df.load(dataflow_file)
True
>>>
>>> # configure an Engine
>>> engine = Engine()
>>> engine.load(df)
True
>>> # extract features from an audio file using AudioFileProcessor
>>> afp = AudioFileProcessor()
>>> afp.processFile(engine,audiofile)
0
>>> feats = engine.readAllOutputs()
>>> # and play with your features
>>>
>>> # extract features from an audio file and write results to csv files
>>> afp.setOutputFormat('csv','output',{'Precision':'8'})
True
>>> afp.processFile(engine,audiofile)
0
>>> # this creates output/myaudio.wav.mfcc.csv,
>>> #              output/myaudio.wav.mfcc_d1.csv and
>>> #              output/myaudio.wav.mfcc_d2.csv files.
>>>
>>> # extract features from a numpy array
>>> import numpy
>>> audio = numpy.random.randn(1,100000)
>>> feats = engine.processAudio(audio)
>>> # and play with your features

See also

yaafelib

Documentation of yaafelib module.

FeaturePlan

More on manipulation feature plans

Engine

Details about metadata and feature extraction

AudioFileProcessor

Extraction features directly from audio files, writing output to files

4.3. yaafe-engine program

The yaafe-engine program is a C++ program to process a dataflow file on a list of audio files. It can produce same outputs as the yaafe.py script. This program is usefull if you need to process feature extraction without any dependency to Python.