The following children nodes delve into the experimental results of the "CRNN for Music Classification" paper. The paper compares the CRNN architecture in this context to the CNN architectures k1c2, k2c2, and k2c1. For more details on these models, see the models child node under "CRNNs for Music Classification" parent node.

Experimental Results of "CRNN for Music Classification" Paper

Four different models were compared: One CRNN and 3 CNNs.  All convolutional and fully connected layers have identical optimization techniques and activation functions - batch normalization and ELU activation function. The CRNN architecture can be compared to the CNN architectures (k1c2, k2c1, k2c2) where 'k' specifies the kernel shape and 'c' specifies the convolutional dimension.

Models of CNN/CRNN in Music Categorization

The children of this node are results of the paper linked to by reference "CONVOLUTIONAL RECURRENT NEURAL NETWORKS FOR MUSIC CLASSIFICATION." It compares common CNN structures to a CRNN structure in the context of music classification.

University of Connecticut

A Convolutional Recurrent Neural Network (CRNN) is a combination of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). 
-CNN are specialized networks for processing data that use a specialized, linear mathematical operation, also known as a convolution. 
-RNN is specialized for processing a sequence of inputs $x^{(1)}, . . . , x^{(τ)}$ and each time adds additional layers of comprehension on top of the previous inputs. 
-The goal of a CRNN is to take advantage of CNNs for local feature extraction and RNNs for temporal summarization of the extracted features.


Convolutional Recurrent Neural Network (CRNN) 

Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017, March). Convolutional recurrent neural networks for music classification. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2392-2396). IEEE.

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