Hi there
I am using below function for feature extraction. Using mfcc and melspectrograms  in my model. Both outputs should be of similar dimension. There is not problem with mfcc alone but I am not getting why model is taking 5 dimensions inmelspectrogram. Can anyone figure It out plz
Regards
class FeatureExtractor(object):
    def __init__(self, rate):
        self.rate = rate

    def get_features(self, features_to_use, X):
        X_features = None
        accepted_features_to_use = ('mfcc', ''melspectrogram')
        if features_to_use not in accepted_features_to_use:
            raise NotImplementedError("{} not in {}!".format(features_to_use, accepted_features_to_use))
        
        if features_to_use in ('mfcc'):
            X_features = self.get_mfcc(X,26)
      
        if features_to_use in ('melspectrogram'):
            X_features = self.get_melspectrogram(X)
      
        return X_features

  


    def get_mfcc(self, X, n_mfcc=13):
        def _get_mfcc(x):
            mfcc_data = librosa.feature.mfcc(x, sr=self.rate, n_mfcc=n_mfcc)
            
            return mfcc_data

        X_features = np.apply_along_axis(_get_mfcc, 1, X)
        return X_features

   

    def get_melspectrogram(self, X):
        def _get_melspectrogram(x):
            mel = librosa.feature.melspectrogram(y=x, sr=self.rate, n_fft=800, hop_length=400)[np.newaxis, :]
            delta = librosa.feature.delta(mel)
            delta_delta = librosa.feature.delta(delta)
            out = np.concatenate((mel, delta, delta_delta))
            return mel

        X_features = np.apply_along_axis(_get_melspectrogram, 1, X)
        return X_features

  

Here the shapes of X is (5984, 32000)