168 research outputs found
AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior
We propose AccoMontage-3, a symbolic music automation system capable of
generating multi-track, full-band accompaniment based on the input of a lead
melody with chords (i.e., a lead sheet). The system contains three modular
components, each modelling a vital aspect of full-band composition. The first
component is a piano arranger that generates piano accompaniment for the lead
sheet by transferring texture styles to the chords using latent chord-texture
disentanglement and heuristic retrieval of texture donors. The second component
orchestrates the piano accompaniment score into full-band arrangement according
to the orchestration style encoded by individual track functions. The third
component, which connects the previous two, is a prior model characterizing the
global structure of orchestration style over the whole piece of music. From end
to end, the system learns to generate full-band accompaniment in a
self-supervised fashion, applying style transfer at two levels of polyphonic
composition: texture and orchestration. Experiments show that our system
outperforms the baselines significantly, and the modular design offers
effective controls in a musically meaningful way
Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls
We propose Polyffusion, a diffusion model that generates polyphonic music
scores by regarding music as image-like piano roll representations. The model
is capable of controllable music generation with two paradigms: internal
control and external control. Internal control refers to the process in which
users pre-define a part of the music and then let the model infill the rest,
similar to the task of masked music generation (or music inpainting). External
control conditions the model with external yet related information, such as
chord, texture, or other features, via the cross-attention mechanism. We show
that by using internal and external controls, Polyffusion unifies a wide range
of music creation tasks, including melody generation given accompaniment,
accompaniment generation given melody, arbitrary music segment inpainting, and
music arrangement given chords or textures. Experimental results show that our
model significantly outperforms existing Transformer and sampling-based
baselines, and using pre-trained disentangled representations as external
conditions yields more effective controls.Comment: In Proceedings of the 24th Conference of the International Society
for Music Information Retrieval (ISMIR 2023), Milan, Ital
Electrospun Nanofibers for Neural Tissue Engineering
Biodegradable nanofibers produced by electrospinning represent a new class of promising scaffolds to support nerve regeneration. We begin with a brief discussion on electrospinning of nanofibers and methods for controlling the structure, porosity, and alignment of the electrospun nanofibers. The methods include control of the nanoscale morphology and microscale alignment for the nanofibers, as well as the fabrication of macroscale, three-dimensional tubular structures. We then highlight recent studies that utilize electrospun nanofibers to manipulate biological processes relevant to nervous tissue regeneration, including stem cell differentiation, guidance of neurite extension, and peripheral nerve injury treatments. The main objective of this feature article is to provide valuable insights into methods for investigating the mechanisms of neurite growth on novel nanofibrous scaffolds and optimization of the nanofiber scaffolds and conduits for repairing peripheral nerve injuries
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique
widely used in hyperspectral images (HSIs) denoising or completion. However,
the uncertainty quantification of the restored HSI has not been addressed to
date. Accurate uncertainty quantification of the denoised HSI facilitates to
applications such as multi-source or multi-scale data fusion, data
assimilation, and product uncertainty quantification, since these applications
require an accurate approach to describe the statistical distributions of the
input data. Therefore, we propose a prior-free closed-form element-wise
uncertainty quantification method for LRMA-based HSI restoration. Our
closed-form algorithm overcomes the difficulty of the HSI patch mixing problem
caused by the sliding-window strategy used in the conventional LRMA process.
The proposed approach only requires the uncertainty of the observed HSI and
provides the uncertainty result relatively rapidly and with similar
computational complexity as the LRMA technique. We conduct extensive
experiments to validate the estimation accuracy of the proposed closed-form
uncertainty approach. The method is robust to at least 10% random impulse noise
at the cost of 10-20% of additional processing time compared to the LRMA. The
experiments indicate that the proposed closed-form uncertainty quantification
method is more applicable to real-world applications than the baseline Monte
Carlo test, which is computationally expensive. The code is available in the
attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and
Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS
Nanofiber Scaffolds with Gradations in Mineral Content for Mimicking the Tendon-to-Bone Insertion Site
We have demonstrated a simple and versatile method for generating a continuously graded, bonelike calcium phosphate coating on a nonwoven mat of electrospun nanofibers. A linear gradient in calcium phosphate content could be achieved across the surface of the nanofiber mat. The gradient had functional consequences with regard to stiffness and biological activity. Specifically, the gradient in mineral content resulted in a gradient in the stiffness of the scaffold and further influenced the activity of mouse preosteoblast MC3T3 cells. This new class of nanofiberbased scaffolds can potentially be employed for repairing the tendon-to-bone insertion site via a tissue engineering approach
Neurite Outgrowth on Nanofiber Scaffolds with Different Orders, Structures, and Surface Properties
Electrospun nanofibers can be readily assembled into various types of scaffolds for applications in neural tissue engineering. The objective of this study is to examine and understand the unique patterns of neurite outgrowth from primary dorsal root ganglia (DRG) cultured on scaffolds of electrospun nanofibers having different orders, structures, and surface properties. We found that the neurites extended radially outward from the DRG main body without specific directionality when cultured on a nonwoven mat of randomly oriented nanofibers. In contrast, the neurites preferentially extended along the long axis of fiber when cultured on a parallel array of aligned nanofibers. When seeded at the border between regions of aligned and random nanofibers, the same DRG simultaneously expressed aligned and random neurite fields in response to the underlying nanofibers. When cultured on a double-layered scaffold where the nanofibers in each layer were aligned along a different direction, the neurites were found to be dependent on the fiber density in both layers. This biaxial pattern clearly demonstrates that neurite outgrowth can be influenced by nanofibers in different layers of a scaffold, rather than the topmost layer only. Taken together, these results will provide valuable information pertaining to the design of nanofiber scaffolds for neuroregenerative applications, as well as the effects of topology on neurite outgrowth, growth cone guidance, and axonal regeneration
Large-sample estimation and inference in multivariate single-index models
By optimizing index functions against different outcomes, we propose a multivariate single-index model (SIM) for development of medical indices that simultaneously work with multiple outcomes. Fitting of a multivariate SIM is not fundamentally different from fitting a univariate SIM, as the former can be written as a sum of multiple univariate SIMs with appropriate indicator functions. What have not been carefully studied are the theoretical properties of the parameter estimators. Because of the lack of asymptotic results, no formal inference procedure has been made available for multivariate SIMs. In this paper, we examine the asymptotic properties of the multivariate SIM parameter estimators. We show that, under mild regularity conditions, estimators for the multivariate SIM parameters are indee
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