.wav-Classification
Classification of .wav audio file using methods for predicting interference like klt, klt_jabloun etc.
Libraries
- Pandas
- sklearn
- Pre-processing
- Linear Model
- Ensemble
- SVM
- Metrics
- Train Test Split
To run
Execute:
- create_dataset_from_original_file.py
- predicted_using_created_dataset.py
Improving Code
The scripts create_dataset_from_original_file.py & predicted_using_created_dataset.py are for basic understanding of Data Wrangling and Pre-Processing.
If you want higher accuracy and have knowledge of python then execute only class_predict_updated.py.
Dataset
data_svm_org_new_v2.csv contains 1793*2 data, varying between 1 - 5.
There are 8 target classes:
- babble_sn5
- car_sn5
- street_sn5
- train_sn5
- babble_sn10
- car_sn10
- street_sn10
- train_sn10.
Each class has 16 samples:
- sp01
- sp02
- sp03
- sp04
- sp06
- sp07
- sp08
- sp09
- sp11
- sp12
- sp13
- sp14
- sp16
- sp17
- sp18
- sp19 (05,10,15 is not there).
Data Wrangling
Data in column[0] is wrangled by delimiter "_".
Splitting data by "_" we create 14 columns that have methods to determine .wav file.
End product of data wrangling is to convert 1793 * 2 into 128 * 14.