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Spectrogram is hazy and hard to distinguish patterns #27

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yusufkhanmohammad opened this issue Mar 18, 2020 · 6 comments
Open

Spectrogram is hazy and hard to distinguish patterns #27

yusufkhanmohammad opened this issue Mar 18, 2020 · 6 comments

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@yusufkhanmohammad
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spectrogram.ipynb uses soundfile library and makes a spectrogram a bit hazy,
tried doing with Librosa amplitude_to_db spectrogram
AtoD = librosa.amplitude_to_db(np.abs(librosa.stft(data)), ref=np.max)

https://colab.research.google.com/drive/1LRdUcsKTiBI-ERVJQ0OEImcUo7WqtjrN

@veirs
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veirs commented Mar 25, 2020 via email

@atreyamaj
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atreyamaj commented Apr 2, 2020

Hey, a good way to start enhancing the spectrogram would be to remove noise from the audio signal before computing the spectrogram (maybe using a package like open-unmix-pytorch).
Some possible ways to enhance the spectrogram are:

  1. band pass filtering
  2. spectral subtraction
  3. using a wiener filter
  4. wavelet packet decomposition

More information can be found in this interesting research paper: http://aircconline.com/sipij/V3N2/3212sipij01.pdf

@kunakl07
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kunakl07 commented Apr 3, 2020

Interesting paper @atreyamaj
Here is the first stage of preprocessing that I performed on S_1 .wav file. I think this notebook will help to get a basic look of preprocessing
https://colab.research.google.com/drive/1ckW8EaIO9Vf1n4KZHz289aryWKrclHtF

@atreyamaj
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This notebook is great! Thanks for sharing it @kunakl07 , I'm sure it'll help others looking to preprocess data too!

@kunakl07
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kunakl07 commented Apr 5, 2020

Welcome @atreyamaj , the actual credits go to Abhishek Singh and Jesse Lopez who performed these preprocessing tasks. I have used it to apply on S_1.wav file which is here.

@atreyamaj
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@yusufkhanmohammad Hey, I just came across a python library that can be used for denoising. The detailed steps for how it does so are given in the documentation.
Link: https://pypi.org/project/noisereduce/

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