9,172 research outputs found
Adversarial Network Bottleneck Features for Noise Robust Speaker Verification
In this paper, we propose a noise robust bottleneck feature representation
which is generated by an adversarial network (AN). The AN includes two cascade
connected networks, an encoding network (EN) and a discriminative network (DN).
Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used
as input to the EN and the output of the EN is used as the noise robust
feature. The EN and DN are trained in turn, namely, when training the DN, noise
types are selected as the training labels and when training the EN, all labels
are set as the same, i.e., the clean speech label, which aims to make the AN
features invariant to noise and thus achieve noise robustness. We evaluate the
performance of the proposed feature on a Gaussian Mixture Model-Universal
Background Model based speaker verification system, and make comparison to MFCC
features of speech enhanced by short-time spectral amplitude minimum mean
square error (STSA-MMSE) and deep neural network-based speech enhancement
(DNN-SE) methods. Experimental results on the RSR2015 database show that the
proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE
and DNN-SE based MFCCs for different noise types and signal-to-noise ratios.
Furthermore, the AN-BN feature is able to improve the speaker verification
performance under the clean condition
Highly Efficient Midinfrared On-Chip Electrical Generation of Graphene Plasmons by Inelastic Electron Tunneling Excitation
Inelastic electron tunneling provides a low-energy pathway for the excitation
of surface plasmons and light emission. We theoretically investigate tunnel
junctions based on metals and graphene. We show that graphene is potentially a
highly efficient material for tunneling excitation of plasmons because of its
narrow plasmon linewidths, strong emission, and large tunability in the
midinfrared wavelength regime. Compared to gold and silver, the enhancement can
be up to 10 times for similar wavelengths and up to 5 orders at their
respective plasmon operating wavelengths. Tunneling excitation of graphene
plasmons promises an efficient technology for on-chip electrical generation and
manipulation of plasmons for graphene-based optoelectronics and nanophotonic
integrated circuits.Comment: 12 pages, 7 figure
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A study of adaptive thermal comfort in a well-controlled climate chamber
This paper aims to critically examine the application of Predicted Mean Vote (PMV) in an air-conditioned environment in the hot-humid climate region. Experimental studies have been conducted in a climate chamber in Chongqing, China, from 2008 to 2010. A total of 440 thermal responses from participants were obtained. Data analysis reveals that the PMV overestimates occupants' mean thermal sensation in the warm environment (PMV > 0) with a mean bias of 0.296 in accordance with the ASHRAE thermal sensation scales. The Bland–Altman method has been applied to assess the agreement of the PMV and Actual Mean Vote (AMV) and reveals a lack of agreement between them. It is identified that habituation due to the past thermal experience of a long-term living in a specific region could stimulate psychological adaptation. The psychological adaptation can neutralize occupants’ actual thermal sensation by moderating the thermal sensibility of the skin. A thermal sensation empirical model and a PMV-revised index are introduced for air-conditioned indoor environments in hot-humid regions. As a result of habituation, the upper limit effective thermal comfort temperature SET* can be increased by 1.6 °C in a warm season based on the existing international standard. As a result, a great potential for energy saving from the air-conditioning system in summer could be achieved
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications
Large protein language models are adept at capturing the underlying
evolutionary information in primary structures, offering significant practical
value for protein engineering. Compared to natural language models, protein
amino acid sequences have a smaller data volume and a limited combinatorial
space. Choosing an appropriate vocabulary size to optimize the pre-trained
model is a pivotal issue. Moreover, despite the wealth of benchmarks and
studies in the natural language community, there remains a lack of a
comprehensive benchmark for systematically evaluating protein language model
quality. Given these challenges, PETA trained language models with 14 different
vocabulary sizes under three tokenization methods. It conducted thousands of
tests on 33 diverse downstream datasets to assess the models' transfer learning
capabilities, incorporating two classification heads and three random seeds to
mitigate potential biases. Extensive experiments indicate that vocabulary sizes
between 50 and 200 optimize the model, whereas sizes exceeding 800
detrimentally affect the model's representational performance. Our code, model
weights and datasets are available at
https://github.com/ginnm/ProteinPretraining.Comment: 46 pages, 4figures, 9 table
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Supplement of the radiance-based method to validate satellite-derived land surface temperature products over heterogeneous land surfaces
Land surface temperature (LST) retrieved from satellite remote sensing data has become a key parameter in research on global environmental change; therefore, the acquisition of accurate satellite-derived LST information is crucial for the diagnosis and analysis of global change. However, it is relatively difficult to obtain the true value of a pixel due to the scale mismatch between in situ measurements and satellite-based observations, especially for commonly heterogeneous and nonisothermal land areas, which greatly increases the difficulty in estimating pixel-representative LST values from in situ measurements for validation of satellite-based LST products. In this study, a supplemented radiance-based (SR-based) validation method was developed to evaluate the latest moderate resolution imaging spectroradiometer (MODIS) Collection 6 Level 2 daily LST/land surface emissivity (LSE) products over a heterogeneous and nonisothermal region of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, West China. In the SR-based framework, pixel-representative LST values are simulated by the MODTRAN model from the corresponding in situ measurements, such as LSE and atmospheric profile measurements, to evaluate the MODIS LST products. The validation results show that the MODIS daytime LST products from the Aqua satellite (MYD11_L2) have a greater accuracy than those from the Terra satellite (MOD11_L2). Analyses of the effect factors indicate a strong correlation between the errors in the MOD11_L2 LST product and the corresponding difference in the MODIS brightness temperature between bands 31 and 32. Although the requirement of synchronous or quasisynchronous in situ measurements for the validated LST products may limit the applicability of the SR-based method, it is still an effective and simple method for validating satellite-derived LST products over mixed pixels. Our method is an indispensable supplement for the validation methods of satellite-derived LST products, and it can be applied in West China and other areas with heterogeneous land surfaces
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