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Deep neural networks have been widely applied in the field of environmental sound classification. However, due to the scarcity of carefully labeled data, their training process suffers from over-fitting. Data augmentation is a technique that alleviates this issue. It augments the training set with synthetic data that are created by modifying some parameters of the real data. However, not all kinds...
Metric learning for music is an important problem for many music information retrieval (MIR) applications such as music generation, analysis, retrieval, classification and recommendation. Traditional music metrics are mostly defined on linear transformations of handcrafted audio features, and may be improper in many situations given the large variety of music styles and instrumentations. In this paper,...
In this paper, we propose a novel method for hand segmentation. To improve the robustness to the wide range of hand appearances and illuminations, we segment the hand area with a superpixels based method instead of a general color model. With the exploitation of the distribution of hand pixels in color space, a distance metric learning stage is designed to promote the segmentation performance. This...
Classification of microarray gene-expression data can potentially help medical diagnosis, and becomes an important topic in bioinformatics. However, microarray data sets are usually of small sample size relative to an overwhelming number of genes. This makes the classification problem fairly challenging. Instance-based learning (IBL) algorithms, such as nearest neighbor (k-NN), are usually the baseline...
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