610 research outputs found
A Fully-differential Electrostatic Micropump with Anti-pull-down Feature
In this poster, a fully-differential electrostatic micropump with anti-pull-down feature is proposed. The micropump has glass-silicon-silicon-glass compound palindromic symmetry structure. Its double membranes can be activated to vibrate simultaneously. Compared to the traditional single-membrane design, the chamber volume and the pumping rate can be doubled. Besides, to overcome pull-down limitation, the proposed micropump has a special design to extend displacement of the membrane without triggering the pull-down effect. The proposed micropump can be used for lab-on-a-chip and micro drug delivery applications
Autoencoder with Group-based Decoder and Multi-task Optimization for Anomalous Sound Detection
In industry, machine anomalous sound detection (ASD) is in great demand.
However, collecting enough abnormal samples is difficult due to the high cost,
which boosts the rapid development of unsupervised ASD algorithms. Autoencoder
(AE) based methods have been widely used for unsupervised ASD, but suffer from
problems including 'shortcut', poor anti-noise ability and sub-optimal quality
of features. To address these challenges, we propose a new AE-based framework
termed AEGM. Specifically, we first insert an auxiliary classifier into AE to
enhance ASD in a multi-task learning manner. Then, we design a group-based
decoder structure, accompanied by an adaptive loss function, to endow the model
with domain-specific knowledge. Results on the DCASE 2021 Task 2 development
set show that our methods achieve a relative improvement of 13.11% and 15.20%
respectively in average AUC over the official AE and MobileNetV2 across test
sets of seven machines.Comment: Submitted to the 2024 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2024
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system
Exploiting effective target modeling units is very important and has always
been a concern in end-to-end automatic speech recognition (ASR). In this work,
we propose a phonetic-assisted multi-target units (PMU) modeling approach, to
enhance the Conformer-Transducer ASR system in a progressive representation
learning manner. Specifically, PMU first uses the pronunciation-assisted
subword modeling (PASM) and byte pair encoding (BPE) to produce
phonetic-induced and text-induced target units separately; Then, three new
frameworks are investigated to enhance the acoustic encoder, including a basic
PMU, a paraCTC and a pcaCTC, they integrate the PASM and BPE units at different
levels for CTC and transducer multi-task training. Experiments on both
LibriSpeech and accented ASR tasks show that, the proposed PMU significantly
outperforms the conventional BPE, it reduces the WER of LibriSpeech clean,
other, and six accented ASR testsets by relative 12.7%, 6.0% and 7.7%,
respectively.Comment: 5 pages, 1 figures, submitted to ICASSP 202
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