Deep learning can be used for audio signal classification in a variety of ways. It can be used
to detect and classify various types of audio signals such as speech, music, and …

Speech Recognition (SR) is an emerging field in the native language nowadays.
Recognizing isolated words in the local language helps people use smartphones and …

We propose a variational Bayesian (VB) approach to learning distributions of latent
variables in deep neural network (DNN) models for cross-domain knowledge transfer, to …

Speech emotion recognition (SER) has many challenges, but one of the main challenges is
that each framework does not have a unified standard. In this paper, we propose SpeechEQ …

Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been
widely applied in many scenarios. However, the training of these models usually poses a …

Network slicing enables operators to efficiently support diverse applications on a shared
infrastructure. However, the evolving complexity of networks, compounded by inter-cell …

In acoustic scene classification (ASC) task, an acoustic scene consists of diverse attributes
and is inferred by identifying combinations of some distinct attributes among them. This …

In this work, we propose a novel variational Bayesian adaptive learning approach for cross-
domain knowledge transfer to address acoustic mismatches between training and testing …

Deep neural networks have become popular in many supervised learning tasks, but they
may suffer from overfitting when the training dataset is limited. To mitigate this, many …

Although deep Neural Networks (DNNs) have achieved tremendous success in audio
classification tasks, their uncertainty calibration are still under-explored. A well-calibrated …