11 accepted papers
We propose a generative model for music extension in this paper. The model is composed of two classifiers, one for music emotion and one for music tonality, and a generative adversarial network (GAN). Therefore, it can generate symbolic music not only based on low level spectral and temporal charact…
Each human cognitive function involves bottom-up and top-down processes. Several methods have been proposed for singing melody extraction by emphasizing either the bottom-up or top-down processes. For hearing, the bottom-up processes include spectral and spectro-temporal decomposition of the sound b…
We proposed a deep neural network (DNN) based approach to synthesize the magnitude of personalized head-related transfer functions (HRTFs) using anthropometric features of the user. To mitigate the over-fitting problem when training dataset is not very large, we built an autoencoder for dimensional…
Inspired by human hearing perception, we propose a two-stage multi-resolution end-to-end model for singing melody extraction in this paper. The convolutional neural network (CNN) is the core of the proposed model to generate multi-resolution representations. The 1-D and 2-D multi-resolution analysis…
Conventional deep neural network (DNN)-based speech enhancement (SE) approaches aim to minimize the mean square error (MSE) between enhanced speech and clean reference. The MSE-optimized model may not directly improve the performance of an automatic speech recognition (ASR) system. If the target is…
Before the era of the neural network (NN), features extracted from auditory models have been applied to various speech applications and been demonstrated more robust against noise than conventional speech-processing features. What's the role of auditory models in the current NN era? Are they obsolet…
In this paper, we build up a hybrid neural network (NN) for singing melody extraction from polyphonic music by imitating human pitch perception. For human hearing, there are two pitch perception models, the spectral model and the temporal model, in accordance with whether harmonics are resolved or n…
Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings t…
Humans analyze sounds not only based on their frequency contents, but also on the temporal variations of the frequency contents. Inspired by auditory perception, we propose a deep neural network (DNN) based dereverberation algorithm in the rate domain, which presents the temporal variations of frequ…
A hearing model, which is parameterized by hearing thresholds, degrees of loudness recruitment and reductions of frequency resolution of a hearing-impaired (HI) patient, is proposed in this paper. The model is developed in the filter-bank framework and is flexible for fitting hearing-loss conditions…
This paper presents a single-channel high-dimensional Wiener filter in the spectro-temporal modulation domain. Unlike other conventional noise reduction techniques, the proposed algorithm not only reduces noise but also enhances the “textures” of the speech signal. A non-iterative decision-directed…