https://github.com/machinelearningzuu/data-engineering-process-of-audio-data

This Repository Consists of the Feature Engineering Process of Audio Signals in both Time Domain & Frequency Domain. In more the repository contains Jupiter-notebook implementations which uses python & librosa
https://github.com/machinelearningzuu/data-engineering-process-of-audio-data

audio-processing librosa machine-learning python

Last synced: 17 days ago
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This Repository Consists of the Feature Engineering Process of Audio Signals in both Time Domain & Frequency Domain. In more the repository contains Jupiter-notebook implementations which uses python & librosa

• Host: GitHub
• URL: https://github.com/machinelearningzuu/data-engineering-process-of-audio-data
• Owner: machinelearningzuu
• Created: 2021-10-19T11:52:47.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-19T12:18:22.000Z (over 3 years ago)
• Last Synced: 2024-12-10T00:27:26.590Z (2 months ago)
• Topics: audio-processing, librosa, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 18.6 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data-Engineering-Process-of-Audio-Data

### Basically we can derive audio features in both Time Domain and Frequency Domain. Here we experiment & visualize different techniques for each audio feature type using python and librosa

## Time Domain Features:

###  1 . Time Spectrum

![github](https://github.com/1zuu/Data-Engineering-Process-of-Audio-Data/blob/main/results/time_spectrum.png)

###  2 . Amplitude Envelope

![github](https://github.com/1zuu/Data-Engineering-Process-of-Audio-Data/blob/main/results/amplitude_envelop.png)

###  3 . Zero Crossing Rate

![github](https://github.com/1zuu/Data-Engineering-Process-of-Audio-Data/blob/main/results/zcr.png)

###  4 . Root Mean Square Energy

![github](https://github.com/1zuu/Data-Engineering-Process-of-Audio-Data/blob/main/results/rmse.png)