Music genres are conventional categories that identify some pieces of music as belonging to a shared tradition or set of conventions. In this paper, we proposed an approach to improve music genre classification with convolutional neural networks (CNN). Using mel-scale spectrogram as the input, we used duplicate convolutional layers whose output will be applied to different pooling layers to provide more statistical information for classification. Also, we made some modifications on residual learning by taking more outputs from convolutional layers. By comparing two different network topologies, our experimental results on the GTZAN dataset show that the proposed method can effectively improve the classification accuracy.
Scoring function (SF) measures the plausibility of triplets in knowledge graphs. Different scoring functions can lead to huge differences in link prediction performances on different knowledge graphs. In this report, we describe a weird scoring function found by random search on the open graph benchmark (OGB). This scoring function, called AutoWeird, only uses tail entity and relation in a triplet to compute its plausibility score. Experimental results show that AutoWeird achieves top-1 performance on ogbl-wikikg2 data set, but has much worse performance than other methods on ogbl-biokg data set. By analyzing the tail entity distribution and evaluation protocol of these two data sets, we attribute the unexpected success of AutoWeird on ogbl-wikikg2 to inappropriate evaluation and concentrated tail entity distribution. Such results may motivate further research on how to accurately evaluate the performance of different link prediction methods for knowledge graphs.
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