405 research outputs found
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
Traditional studies on voice conversion (VC) have made progress with parallel
training data and known speakers. Good voice conversion quality is obtained by
exploring better alignment modules or expressive mapping functions. In this
study, we investigate zero-shot VC from a novel perspective of self-supervised
disentangled speech representation learning. Specifically, we achieve the
disentanglement by balancing the information flow between global speaker
representation and time-varying content representation in a sequential
variational autoencoder (VAE). A zero-shot voice conversion is performed by
feeding an arbitrary speaker embedding and content embeddings to the VAE
decoder. Besides that, an on-the-fly data augmentation training strategy is
applied to make the learned representation noise invariant. On TIMIT and VCTK
datasets, we achieve state-of-the-art performance on both objective evaluation,
i.e., speaker verification (SV) on speaker embedding and content embedding, and
subjective evaluation, i.e., voice naturalness and similarity, and remains to
be robust even with noisy source/target utterances.Comment: Accepted to 2022 ICASS
Sparse Complementary Pairs with Additional Aperiodic ZCZ Property
This paper presents a novel class of complex-valued sparse complementary
pairs (SCPs), each consisting of a number of zero values and with additional
zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and
crosscorrelations of the two constituent sequences. Direct constructions of
SCPs and their mutually-orthogonal mates based on restricted generalized
Boolean functions are proposed. It is shown that such SCPs exist with arbitrary
lengths and controllable sparsity levels, making them a disruptive sequence
candidate for modern low-complexity, low-latency, and low-storage signal
processing applications
Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network
Network traffic classification is the basis of many network security
applications and has attracted enough attention in the field of cyberspace
security. Existing network traffic classification based on convolutional neural
networks (CNNs) often emphasizes local patterns of traffic data while ignoring
global information associations. In this paper, we propose a MLP-Mixer based
multi-view multi-label neural network for network traffic classification.
Compared with the existing CNN-based methods, our method adopts the MLP-Mixer
structure, which is more in line with the structure of the packet than the
conventional convolution operation. In our method, the packet is divided into
the packet header and the packet body, together with the flow features of the
packet as input from different views. We utilize a multi-label setting to learn
different scenarios simultaneously to improve the classification performance by
exploiting the correlations between different scenarios. Taking advantage of
the above characteristics, we propose an end-to-end network traffic
classification method. We conduct experiments on three public datasets, and the
experimental results show that our method can achieve superior performance.Comment: 15 pages,6 figure
DEM-CFD analysis of contact electrification and electrostatic interactions during fluidization
Contact electrification and electrostatic interactions often occur in the fluidization process, which can significantly influence the dynamic behaviour of particles and the fluidization performance. In this study, a discrete element method coupled with computational fluid dynamics (DEM-CFD) is developed by implementing contact electrification and electrostatic interaction models and the combined effects of contact electrification and electrostatic interaction on fluidization are analysed. It is found that the charge of the particle system increase with the superficial gas velocity. Particles of different material properties (especially work function) can be bi-charged and form agglomerates. At low superficial gas velocities, the particle bed cannot be fully fluidized and the pressure drop tends to be stable rather than fluctuating as the gas flows through the micro-channels of agglomerates. However, at high superficial gas velocities, the agglomerates can break, inducing strong fluctuation of pressure drop. Clearly, the electrostatic phenomena and fluidization behaviour can mutually influence each other during the process
- …