Unsupervised Learning to cluster acoustic signals/spectrograms
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Hello,
I'm not new to Matlab but completely new to Machine Learning, and while I understand the theory behind ML, I have never applied it and feel quite overwhelmed.
I have a large number of audio clips, each with a signal of the same general type/source. I want to use Unsupervised Learning to cluster those signals/their spectrograms based on (visual) differences among them. We do not want to have to train a model ourselves, first.
I have a hard time conceptualizing my approach and the corresponding code. Can anyone point me in the direction of a tutorial or example(s) for this specific use case or similar ones (possibly images instead of spectrograms?)?
Thank you!
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Varun Sai Alaparthi
2023-1-18
Hello Anke,
I understand you are looking for MATLAB examples for clustering-based approaches for image data. There are various algorithms for unsupervised clustering like k-means which you can use directly for this task by inputting the double values of the images.
But the most common and efficient approach for images/spectrograms is to take features from a trained model and apply k-means on top of those for clustering.
You could try using an Autoencoder which can be used to get a representation of given input data which can be an image, text, or videos.
The Autoencoder will generate meaningful features of the images, and these features can be extracted from the last layer of encoder. The last layer of encoder is a lower dimensional representation of the image which can be used in k-means for clustering the features in K groups. This way the images will be clustered into several groups.
If you have any further queries, please feel free to reply to my answer
Sincerely,
Varun
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