17,274 research outputs found
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP
More on complexity of operators in quantum field theory
Recently it has been shown that the complexity of SU() operator is
determined by the geodesic length in a bi-invariant Finsler geometry, which is
constrained by some symmetries of quantum field theory. It is based on three
axioms and one assumption regarding the complexity in continuous systems. By
relaxing one axiom and an assumption, we find that the complexity formula is
naturally generalized to the Schatten -norm type. We also clarify the
relation between our complexity and other works. First, we show that our
results in a bi-invariant geometry are consistent with the ones in a
right-invariant geometry such as -local geometry. Here, a careful analysis
of the sectional curvature is crucial. Second, we show that our complexity can
concretely realize the conjectured pattern of the time-evolution of the
complexity: the linear growth up to saturation time. The saturation time can be
estimated by the relation between the topology and curvature of SU() groups.Comment: Modified the Sec. 4.1, where we offered a powerful proof: if (1) the
ket vector and bra vector in quantum mechanics contain same physics, or (2)
adding divergent terms to a Lagrangian will not change underlying physics,
then complexity in quantum mechanics must be bi-invariant
Principles and symmetries of complexity in quantum field theory
Based on general and minimal properties of the {\it discrete} circuit
complexity, we define the complexity in {\it continuous} systems in a
geometrical way. We first show that the Finsler metric naturally emerges in the
geometry of the complexity in continuous systems. Due to fundamental symmetries
of quantum field theories, the Finsler metric is more constrained and
consequently, the complexity of SU() operators is uniquely determined as a
length of a geodesic in the Finsler geometry. Our Finsler metric is
bi-invariant contrary to the right-invariance of discrete qubit systems. We
clarify why the bi-invariance is relevant in quantum field theoretic systems.
After comparing our results with discrete qubit systems we show most results in
-local right-invariant metric can also appear in our framework. Based on the
bi-invariance of our formalism, we propose a new interpretation for the
Schr\"{o}dinger's equation in isolated systems - the quantum state evolves by
the process of minimizing "computational cost."Comment: Published version; added a short introduction on Finsler geometr
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