Process melspectrograms with convolutional neural network
$begingroup$
I am trying to do audio classification with a convolutional neural network. There are six classes. With librosa, I have created melspectrograms for the one second long .wav audio files. It returned 640x480 .jpg files.
My question is now how to proceed with the input, since I think it is too large as input for the network. If so, what would an adequate resolution be? Something around 60x60? Does it even have to be quadratic?
Options from my perspective:
- Re-encode melspectrograms from librosa with smaller resolution
- Use cv2 and simply do a cv2.resize() before passing it to the input layer.
- Leave the resolution untouched, and introduce more convolutional layers.
- ?
python audio-recognition
asked 2 days ago
harrisonfooordharrisonfooord
62
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$begingroup$
I am trying to do audio classification with a convolutional neural network. There are six classes. With librosa, I have created melspectrograms for the one second long .wav audio files. It returned 640x480 .jpg files.
My question is now how to proceed with the input, since I think it is too large as input for the network. If so, what would an adequate resolution be? Something around 60x60? Does it even have to be quadratic?
Options from my perspective:
- Re-encode melspectrograms from librosa with smaller resolution
- Use cv2 and simply do a cv2.resize() before passing it to the input layer.
- Leave the resolution untouched, and introduce more convolutional layers.
- ?
python audio-recognition
asked 2 days ago
harrisonfooordharrisonfooord
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am trying to do audio classification with a convolutional neural network. There are six classes. With librosa, I have created melspectrograms for the one second long .wav audio files. It returned 640x480 .jpg files.
My question is now how to proceed with the input, since I think it is too large as input for the network. If so, what would an adequate resolution be? Something around 60x60? Does it even have to be quadratic?
Options from my perspective:
- Re-encode melspectrograms from librosa with smaller resolution
- Use cv2 and simply do a cv2.resize() before passing it to the input layer.
- Leave the resolution untouched, and introduce more convolutional layers.
- ?
python audio-recognition
asked 2 days ago
harrisonfooordharrisonfooord
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to do audio classification with a convolutional neural network. There are six classes. With librosa, I have created melspectrograms for the one second long .wav audio files. It returned 640x480 .jpg files.
My question is now how to proceed with the input, since I think it is too large as input for the network. If so, what would an adequate resolution be? Something around 60x60? Does it even have to be quadratic?
Options from my perspective:
- Re-encode melspectrograms from librosa with smaller resolution
- Use cv2 and simply do a cv2.resize() before passing it to the input layer.
- Leave the resolution untouched, and introduce more convolutional layers.
- ?
python audio-recognition
python audio-recognition
asked 2 days ago
harrisonfooordharrisonfooord
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
harrisonfooordharrisonfooord
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
harrisonfooordharrisonfooord
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
harrisonfooordharrisonfooord
62
asked 2 days ago
harrisonfooordharrisonfooord
62
62
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
harrisonfooord is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Leave the resolution untouched, and introduce more convolutional
layers.
This should be the next step. Two primary reasons for it :
- This should reduce number of trainable parameters
- Model can learn more abstract features
answered 2 days ago
Shamit VermaShamit Verma
78426
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$begingroup$
Leave the resolution untouched, and introduce more convolutional
layers.
This should be the next step. Two primary reasons for it :
- This should reduce number of trainable parameters
- Model can learn more abstract features
answered 2 days ago
Shamit VermaShamit Verma
78426
$endgroup$
add a comment |
$begingroup$
Leave the resolution untouched, and introduce more convolutional
layers.
This should be the next step. Two primary reasons for it :
- This should reduce number of trainable parameters
- Model can learn more abstract features
answered 2 days ago
Shamit VermaShamit Verma
78426
$endgroup$
add a comment |
$begingroup$
Leave the resolution untouched, and introduce more convolutional
layers.
This should be the next step. Two primary reasons for it :
- This should reduce number of trainable parameters
- Model can learn more abstract features
answered 2 days ago
Shamit VermaShamit Verma
78426
$endgroup$
Leave the resolution untouched, and introduce more convolutional
layers.
This should be the next step. Two primary reasons for it :
- This should reduce number of trainable parameters
- Model can learn more abstract features
answered 2 days ago
Shamit VermaShamit Verma
78426
answered 2 days ago
Shamit VermaShamit Verma
78426
answered 2 days ago
Shamit VermaShamit Verma
78426
answered 2 days ago
Shamit VermaShamit Verma
78426
78426
add a comment |
add a comment |
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harrisonfooord is a new contributor. Be nice, and check out our Code of Conduct.
harrisonfooord is a new contributor. Be nice, and check out our Code of Conduct.
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