Skip to content

This is a audio classification Project using python Libraries such as librosa to make the visual representation of the audio files, and using numpy to make array of data for manipulation and then extraction the features for classification to train and test of CNN model.

Notifications You must be signed in to change notification settings

Kavayk29/Audio-classification-using-Python-Library

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Audio-classification-using-Python-Library

This project focuses on audio processing and analysis using Python libraries like librosa and matplotlib. It includes tasks such as loading audio files, visualizing waveforms, and analyzing audio characteristics like sample rate and channels. The project is a comprehensive exploration of audio data manipulation, with potential applications in audio classification, signal processing, and machine learning.

Key Features: Audio Visualization: Load and visualize audio waveforms to understand the structure and characteristics of sound signals. Data Handling: Import and manage metadata associated with audio files, enabling detailed analysis and processing. Exploratory Analysis: Investigate various audio features, including sample rate and channel information, to better understand the data. Technologies Used: Python: The primary language for all data processing and analysis tasks. Librosa: A powerful library for audio analysis and manipulation. Matplotlib: Used for visualizing audio waveforms and other relevant data. This project is ideal for anyone interested in exploring the fundamentals of audio processing and its applications in various domains like machine learning and signal processing.

About

This is a audio classification Project using python Libraries such as librosa to make the visual representation of the audio files, and using numpy to make array of data for manipulation and then extraction the features for classification to train and test of CNN model.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published