In the figure shown, where higher AUC is better, CRNN outperforms k2c1 in 44/50 tags, and k2c1 outperforms k1c2 in 48/50 tags.

University of California, Santa Cruz

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

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.

CONVOLUTIONAL RECURRENT NEURAL NETWORKS FOR MUSIC CLASSIFICATION

When compared to other architectures, where the bottleneck of the system is memory usage, the CRNN outperforms the others. CRNN consistently outperforming the k2c2 model - which shares the same 2D structure - may indicate that learning a global structure is more important than focusing on local structures for summarization.

Memory Usage in CNN/CRNN Model Comparison

AUC in CNN/CRNN Model Comparison

When compared to other architectures in terms of computation time, k2c2 and CRNN have similar results and are better than k1c1 and k2c1. Training speed, however, is ranked as such: k2c1 > k2c2 > k1c2 > CRNN. There are two factors that likely lead to this (child nodes).

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