269 research outputs found
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Recent work has shown that the end-to-end approach using convolutional neural
network (CNN) is effective in various types of machine learning tasks. For
audio signals, the approach takes raw waveforms as input using an 1-D
convolution layer. In this paper, we improve the 1-D CNN architecture for music
auto-tagging by adopting building blocks from state-of-the-art image
classification models, ResNets and SENets, and adding multi-level feature
aggregation to it. We compare different combinations of the modules in building
CNN architectures. The results show that they achieve significant improvements
over previous state-of-the-art models on the MagnaTagATune dataset and
comparable results on Million Song Dataset. Furthermore, we analyze and
visualize our model to show how the 1-D CNN operates.Comment: Accepted for publication at ICASSP 201
A Second-order bias model for the Logarithmic Halo Mass Density
We present an analytic model for the local bias of dark matter halos in a
LCDM universe. The model uses the halo mass density instead of the halo number
density and is searched for various halo mass cuts, smoothing lengths, and
redshift epoches. We find that, when the logarithmic density is used, the
second-order polynomial can fit the numerical relation between the halo mass
distribution and the underlying matter distribution extremely well. In this
model the logarithm of the dark matter density is expanded in terms of log halo
mass density to the second order. The model remains excellent for all halo mass
cuts (from M_{cut}=3\times10^{11}3\times10^{12}h^{-1}M_{\odot}R=5h^{-1}50h^{-1}$Mpc), and redshift ranges
(from z=0 to 1.0) considered in this study. The stochastic term in the relation
is found not entirely random, but a part of the term can be determined by the
magnitude of the shear tensor.Comment: 8 pages, 7 figures, accepted for publication on Ap
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