983 research outputs found
Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons
The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations.
We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis
A novel isospectral deformation chain in supersymmetric quantum mechanics
We present a novel isospectral deformation chain in supersymmetric quantum
mechanics. The chain is applied to the Coulomb potential
Geometry induced potential on a 2D-section of a wormhole: catenoid
We show that a two dimensional wormhole geometry is equivalent to a catenoid,
a minimal surface. We then obtain the curvature induced geometric potential and
show that the ground state with zero energy corresponds to a reflectionless
potential. By introducing an appropriate coordinate system we also obtain bound
states for different angular momentum channels. Our findings can be realized in
suitably bent bilayer graphene sheets with a neck or in a honeycomb lattice
with an array of dislocations or in nanoscale waveguides in the shape of a
catenoid.Comment: to appear in Phys.Rev.
Perspectives on Bayesian Optimization for HCI
In this position paper we discuss optimization in the HCI
domain based on our experiences with Bayesian methods
for modeling and optimization of audio systems, including
challenges related to evaluating, designing, and optimizing
such interfaces. We outline and demonstrate how a
combined Bayesian modeling and optimization approach
provides a flexible framework for integrating various user
and content attributes, while also supporting model-based
optimization of HCI systems. Finally, we discuss current
and future research direction and applications, such as
inferring user needs and optimizing interfaces for
computer assisted teaching
A nonsmooth primal-dual method with simultaneous adaptive PDE constraint solver
We introduce an efficient first-order primal-dual method for the solution of
nonsmooth PDE-constrained optimization problems. We achieve this efficiency
through not solving the PDE or its linearisation on each iteration of the
optimization method. Instead, we run the method in parallel with a simple
conventional linear system solver (Jacobi, Gauss-Seidel, conjugate gradients),
always taking only one step of the linear system solver for each step of the
optimization method. The control parameter is updated on each iteration as
determined by the optimization method. We prove linear convergence under a
second-order growth condition, and numerically demonstrate the performance on a
variety of PDEs related to inverse problems involving boundary measurements
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