716 research outputs found
Multi-layered Cepstrum for Instantaneous Frequency Estimation
We propose the multi-layered cepstrum (MLC) method to estimate multiple
fundamental frequencies (MF0) of a signal under challenging contamination such
as high-pass filter noise. Taking the operation of cepstrum (i.e., Fourier
transform, filtering, and nonlinear activation) recursively, MLC is shown as an
efficient method to enhance MF0 saliency in a step-by-step manner. Evaluation
on a real-world polyphonic music dataset under both normal and low-fidelity
conditions demonstrates the potential of MLC.Comment: In 2018 6th IEEE Global Conference on Signal and Information
Processin
Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables
This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for
singing voice synthesis (SVS) that exploits the physical characteristics of the
human voice using differentiable digital signal processing. GOLF employs a
glottal model as the harmonic source and IIR filters to simulate the vocal
tract, resulting in an interpretable and efficient approach. We show it is
competitive with state-of-the-art singing voice vocoders, requiring fewer
synthesis parameters and less memory to train, and runs an order of magnitude
faster for inference. Additionally, we demonstrate that GOLF can model the
phase components of the human voice, which has immense potential for rendering
and analysing singing voices in a differentiable manner. Our results highlight
the effectiveness of incorporating the physical properties of the human voice
mechanism into SVS and underscore the advantages of signal-processing-based
approaches, which offer greater interpretability and efficiency in synthesis.
Audio samples are available at https://yoyololicon.github.io/golf-demo/.Comment: 9 pages, 4 figures. Accepted at ISMIR 202
Zero-Shot Duet Singing Voices Separation with Diffusion Models
In recent studies, diffusion models have shown promise as priors for solving
audio inverse problems. These models allow us to sample from the posterior
distribution of a target signal given an observed signal by manipulating the
diffusion process. However, when separating audio sources of the same type,
such as duet singing voices, the prior learned by the diffusion process may not
be sufficient to maintain the consistency of the source identity in the
separated audio. For example, the singer may change from one to another
occasionally. Tackling this problem will be useful for separating sources in a
choir, or a mixture of multiple instruments with similar timbre, without
acquiring large amounts of paired data. In this paper, we examine this problem
in the context of duet singing voices separation, and propose a method to
enforce the coherency of singer identity by splitting the mixture into
overlapping segments and performing posterior sampling in an auto-regressive
manner, conditioning on the previous segment. We evaluate the proposed method
on the MedleyVox dataset and show that the proposed method outperforms the
naive posterior sampling baseline. Our source code and the pre-trained model
are publicly available at https://github.com/yoyololicon/duet-svs-diffusion.Comment: 9 pages, 1 figure. Published at Sound Demixing Workshop 202
Combining Coauthorship Network and Content for Literature Recommendation
This paper studies literature recommendation approaches using both content features and coauthorship relations of articles in literature databases. Most literature databases allow data access (via site subscription) without having to identify users, and thus task-focused recommendation is more appropriate in this context. Previous work mostly utilizes content and usage log for making task-focused recommendation. More recent works start to incorporate coauthorship network for recommendation and found it beneficial when the specified articles preferred by authors are similar in their content. However, it was also found that recommendation based on content features achieves better performance under other circumstances. Therefore, in this work we propose to incorporate both content and coauthorship network in making task-focused recommendation. Three hybrid methods, namely switching, proportional, and fusion are developed and compared. Our experimental results show that in general the proposed hybrid approach achieves better performance than approaches that utilize only one source of knowledge. In particular, the fusion method tends to have higher recommendation accuracy for articles of higher ranks. Besides, the content-based approach is more likely to recommend articles of low fidelity, whereas the coauthorship network-based approach has the least chance
Toona sinensis
Toona sinensis leaf (TSL) is commonly used as a vegetable and in spice in Asia. In this study, feeding with aqueous extract of TSL (TSL-A) alleviated oxidative stress and recovered the motility and functions of sperm in rats under oxidative stress. Protein expressions in testes identified by proteomic analysis and verified by Western blot demonstrated that TSL-A not only downregulated the level of glutathione transferase mu6 (antioxidant system), heat shock protein 90 kDa-β (protein misfolding repairing system), cofilin 2 (spermatogenesis), and cyclophilin A (apoptosis) but also upregulated crease3-hydroxy-3-methylglutaryl-coenzyme A synthase 2 (steroidogenesis), heat shock glycoprotein 96, and pancreatic trypsin 1 (sperm-oocyte interaction). These results indicate that TSL-A promotes the functions of sperm and testes via regulating multiple testicular proteins in rats under oxidative stress, suggesting that TSL-A is a valuable functional food supplement to improve functions of sperm and testes for males under oxidative stress
Multi-Decadal Change of Atmospheric Aerosols and Their Effect on Surface Radiation
We present an investigation on multi-decadal changes of atmospheric aerosols and their effects on surface radiation using a global chemistry transport model along with the near-term to long-term data records. We focus on a 28-year time period of satellite era from 1980 to 2007, during which a suite of aerosol data from satellite observations and ground-based remote sensing and in-situ measurements have become available. We analyze the long-term global and regional aerosol optical depth and concentration trends and their relationship to the changes of emissions" and assess the role aerosols play in the multi-decadal change of solar radiation reaching the surface (known as "dimming" or "brightening") at different regions of the world, including the major anthropogenic source regions (North America, Europe, Asia) that have been experiencing considerable changes of emissions, dust and biomass burning regions that have large interannual variabilities, downwind regions that are directly affected by the changes in the source area, and remote regions that are considered to representing "background" conditions
Multi-Decadal Change of Atmospheric Aerosols and their Effect on Surface Radiation
We present an investigation on multi-decadal changes of atmospheric aerosols and their effects on surface radiation using a global chemistry transport model, GOCART, along with the near-term to long-term data records. We focus on a 28-year time period of satellite era from 1980 to 2007 during which a suite of aerosol data from satellite observations, ground-based measurements, and intensive field experiments have become available. Particularly: (1) We compare the model calculated clear sky downward radiation at the surface with surface network data from BSRN and CMA (2) We compare the model and surface data with satellite derived downward radiation products from ISCCP and SRS (3) We analyze the long-term global and regional aerosol trends in major anthropogenic source regions (North America, Europe, Asia) that have been experiencing considerable changes of emissions during the three decades, dust and biomass burning regions that have large interannual variability, downwind regions that are directly affected by the changes in the source area, and remote regions that are considered to representing "background" conditions. The comparisons and methods from this study can be applied to multiple model analysis in the AeroCom framework
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