5 accepted papers
Dialect recognition aims to recognize dialect categories in utterances, which has been applied in many audio applications. Recently, various Time Delayed Neural Network (TDNN) based AI models are proposed to solve dialect recognition problems, such as D-TDNN, DMC-TDNN, and ECAPA-TDNN, however, most…
CNN+RNN models have become the mainstream approach for semi-supervised sound event detection, and the CNN part is mainly a stack of several 2D convolutional layers to capture the representations of the time-frequency features. However, conventional 2D convolution is of limited ability in capturing d…
In this paper, we propose a Conformer-based network to improve the performance of multi-task audio source separation. This network, named EAD-Conformer, employs Conformer blocks to capture both local and global information, and an encoder-attention-decoder manner encourages the network to perform at…
Automatic singing evaluation without reference melody has long been a difficult problem. This paper aims to pilot a novel data driven approach to tackle this artistic problem. We constructed a large scale dataset and designed an innovative Bi-Dense neural network which can address this task efficien…
We propose a sparse hidden Markov model (HMM)-based single-channel speech enhancement method that models the speech and noise gains accurately in both stationary and nonstationary environments. The objective function is augmented with an lp regularization term resulting in a sparse autoregressive HM…