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Spectrogram is hazy and hard to distinguish patterns #27
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Mohammad,
I suggest you try to make a spectrogram from just the first file in the
flac directory. This first call we term S 1 and is the most common call
that the local orca use.
It should give you a spectrogram similar to the one below I just made with
Audacity from that first file.
Val
[image: image.png]
…On Wed, Mar 18, 2020 at 12:00 PM Mohammad Yusuf Khan < ***@***.***> wrote:
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
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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).
More information can be found in this interesting research paper: http://aircconline.com/sipij/V3N2/3212sipij01.pdf |
Interesting paper @atreyamaj |
This notebook is great! Thanks for sharing it @kunakl07 , I'm sure it'll help others looking to preprocess data too! |
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. |
@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. |
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
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