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I am trying to do 10-fold cross-validation on an audio dataset. The audio is clipped into small segments, so we have multiple clips from the same file. To avoid overfitting, each audio is assigned to a particular fold. The file structure is similar to the UrbanSoundDataset. I am generating MFCC features for each fold and saving the features using the following code:

   np.savez("{0}/{1}_mfcc".format(save_dir, "fold"+str(fold_id)), features=X, 
             labels=y)

The feature set for each fold is fixed to rows x 40 mfcc x 174 dimensions. For example, the dimension for fold 1 is (534, 40, 174), and the dimension for fold 2 is (538, 40, 174). When I load the fold values, I want to stack the 9 folds together for training. For example,if I stack fold1 and fold2 in the above example, I should have (1072,40,174) length array at the end of the stacking process.

How can I do that using numpy?

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  • $\begingroup$ You can use numpy.concatenate to concatenate the two arrays on the first dimension. $\endgroup$ Commented Nov 10, 2021 at 9:20

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I think what you are looking for is numpy.concatenate.

From the documentation this allows you to:

Join a sequence of arrays along an existing axis.

It has the function signature:

numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")

See the documentation for more.

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