- Extraction of the features from the Audio files.
- Scaled the features. (I also normalized just to compare which was better for my data set.)
- Used the Imputer for any missing data.
- Then use the SVM to classify the data.
I find that the classification is average to say the least. The precision and recall are about 50%. I have also tried using the decision trees classifiers and also the k-neighbours technique, but none seems to improve over the SVM.
Research papers suggest that using the SVM should yield values of precision close to 90%.
I do not know why the values are so low! Maybe I am missing something.
Can someone tell me if there is a general procedure to follow while performing Audio/Acoustic Classification ?
Any suggestions in the right direction will be helpful.
Since the comment section takes only limited characters : here is some more information -
- I have to classify the given sounds into environmental classes - street music or siren etc. I think you can get the picture here.
- I have extracted the information : the feature matrices from the sound files. Extract meaning I get the MFCC, the Spectral Flatness Measure and several other features (in total 10) that are needed to classify a signal. I push them into a numpy array using python.
- Several of the features are multi-dimensional like the MFCC which has a min of 15 dimensions.
- Each of the sounds are stored in a folder with a specific name : When I read the sound file to extract the information/featureMatrix, I assign a target/classID to each of the sounds - stored as target in a numpy array.
Then I perform some pre-processing to remove the empty values and normalize the data before I feed the feature matrix into the SVM.
My intention is to use these features and classify the sounds into a specific class. The classifier must read a feature as input one. The other input for the classifier will be the identification number I assigned while reading it from the directory of sound files.
The SVM was able to classify the sounds but not with high precision which was max of 49% !
Please tell me if this was enough.