https://github.com/sarcode/.wav-classification
Classification of .wav audio file using methods for predicting interference like klt, klt_jabloun etc.
https://github.com/sarcode/.wav-classification
Last synced: 9 months ago
JSON representation
Classification of .wav audio file using methods for predicting interference like klt, klt_jabloun etc.
- Host: GitHub
- URL: https://github.com/sarcode/.wav-classification
- Owner: SarCode
- Created: 2019-02-07T17:02:29.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-04-21T22:58:43.000Z (almost 3 years ago)
- Last Synced: 2025-04-11T03:17:13.474Z (12 months ago)
- Language: Python
- Size: 27.3 KB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[![LinkedIn][linkedin-shield]][linkedin-url]
# .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.
## Machine Learning Models ##
* [Logistic Regression]()
* [Random Forest Classifier]()
* [SVC]()
## Performance Metric ##
[Accuracy Score]()
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/sarthak-agarwal-dell/