391 research outputs found
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
An effective approach to non-parallel voice conversion (VC) is to utilize
deep neural networks (DNNs), specifically variational auto encoders (VAEs), to
model the latent structure of speech in an unsupervised manner. A previous
study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for
VC. How- ever, VAE using other types of spectral features such as mel- cepstral
coefficients (MCCs), which are related to human per- ception and have been
widely used in VC, have not been prop- erly investigated. Instead of using one
specific type of spectral feature, it is expected that VAE may benefit from
using multi- ple types of spectral features simultaneously, thereby improving
the capability of VAE for VC. To this end, we propose a novel VAE framework
(called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework
utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple
objectives in order to constrain the behavior of the learned encoder and de-
coder. Experimental results demonstrate that the proposed CD- VAE framework
outperforms the conventional VAE framework in terms of subjective tests.Comment: Accepted to ISCSLP 201
Poly[diaqua-μ4-biphenyl-4,4′-dicarboxylato-magnesium(II)]
The solvothermal reaction of magnesium nitrate with biphenyl-4,4′-dicarboxylic acid in N,N-dimethylformamide and water leads to the formation of crystals of the title complex, [Mg(C14H8O4)(H2O)2]n. In the crystal structure, the Mg cations are coordinated by six O atoms from two water molecules and four symmetry-related biphenyl-4,4′-dicarboxylate anions within slightly distorted octahedra. The Mg cations are located on a center of inversion, the biphenyl-4,4′-dicarboxylate anions around a twofold rotation axis and the water molecule in a general position. The Mg cations are linked by the anions into a three-dimensional framework
Transformer-based Image Compression with Variable Image Quality Objectives
This paper presents a Transformer-based image compression system that allows
for a variable image quality objective according to the user's preference.
Optimizing a learned codec for different quality objectives leads to
reconstructed images with varying visual characteristics. Our method provides
the user with the flexibility to choose a trade-off between two image quality
objectives using a single, shared model. Motivated by the success of
prompt-tuning techniques, we introduce prompt tokens to condition our
Transformer-based autoencoder. These prompt tokens are generated adaptively
based on the user's preference and input image through learning a prompt
generation network. Extensive experiments on commonly used quality metrics
demonstrate the effectiveness of our method in adapting the encoding and/or
decoding processes to a variable quality objective. While offering the
additional flexibility, our proposed method performs comparably to the
single-objective methods in terms of rate-distortion performance
Transformer-based Variable-rate Image Compression with Region-of-interest Control
This paper proposes a transformer-based learned image compression system. It
is capable of achieving variable-rate compression with a single model while
supporting the region-of-interest (ROI) functionality. Inspired by prompt
tuning, we introduce prompt generation networks to condition the
transformer-based autoencoder of compression. Our prompt generation networks
generate content-adaptive tokens according to the input image, an ROI mask, and
a rate parameter. The separation of the ROI mask and the rate parameter allows
an intuitive way to achieve variable-rate and ROI coding simultaneously.
Extensive experiments validate the effectiveness of our proposed method and
confirm its superiority over the other competing methods.Comment: Accepted to IEEE ICIP 202
TransTIC: Transferring Transformer-based Image Compression from Human Visualization to Machine Perception
This work aims for transferring a Transformer-based image compression codec
from human vision to machine perception without fine-tuning the codec. We
propose a transferable Transformer-based image compression framework, termed
TransTIC. Inspired by visual prompt tuning, we propose an instance-specific
prompt generator to inject instance-specific prompts to the encoder and
task-specific prompts to the decoder. Extensive experiments show that our
proposed method is capable of transferring the codec to various machine tasks
and outshining the competing methods significantly. To our best knowledge, this
work is the first attempt to utilize prompting on the low-level image
compression task
Evaluating Self-supervised Speech Models on a Taiwanese Hokkien Corpus
Taiwanese Hokkien is declining in use and status due to a language shift
towards Mandarin in Taiwan. This is partly why it is a low resource language in
NLP and speech research today. To ensure that the state of the art in speech
processing does not leave Taiwanese Hokkien behind, we contribute a 1.5-hour
dataset of Taiwanese Hokkien to ML-SUPERB's hidden set. Evaluating ML-SUPERB's
suite of self-supervised learning (SSL) speech representations on our dataset,
we find that model size does not consistently determine performance. In fact,
certain smaller models outperform larger ones. Furthermore, linguistic
alignment between pretraining data and the target language plays a crucial
role.Comment: Accepted to ASRU 202
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