Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Grais, Emad M."'
Autor:
Grais, Emad M., Wang, Xiaoya, Wang, Jie, Zhao, Fei, Jiang, Wen, Cai, Yuexin, Zhang, Lifang, Lin, Qingwen, Yang, Haidi
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) too
Externí odkaz:
http://arxiv.org/abs/2103.02982
Autor:
Grais, Emad M., Nie, Leixin, Zou, Bin, Wang, Xiaoya, Rahim, Tariq, Sun, Jing, Li, Shuna, Wang, Jie, Jiang, Wen, Cai, Yuexin, Yang, Haidi, Zhao, Fei
Publikováno v:
In Biomedical Signal Processing and Control January 2024 87 Part A
Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good feat
Externí odkaz:
http://arxiv.org/abs/1910.09266
Publikováno v:
This paper will be presented at EUSIPCO 2019
Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals. Therefore, common performance evaluation toolkits are not applicable to real-world situations where the ground
Externí odkaz:
http://arxiv.org/abs/1811.00454
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for traini
Externí odkaz:
http://arxiv.org/abs/1803.00702
In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global information from the input features, while layers with small RF size ca
Externí odkaz:
http://arxiv.org/abs/1710.11473
Autor:
Grais, Emad M., Plumbley, Mark D.
Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the
Externí odkaz:
http://arxiv.org/abs/1703.08019
The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To tackle this problem, we propose to enhance the separated sources to d
Externí odkaz:
http://arxiv.org/abs/1609.01678
In this paper, a novel approach for single channel source separation (SCSS) using a deep neural network (DNN) architecture is introduced. Unlike previous studies in which DNN and other classifiers were used for classifying time-frequency bins to obta
Externí odkaz:
http://arxiv.org/abs/1311.2746
Autor:
Grais, Emad M., Erdogan, Hakan
We propose a new method to enforce priors on the solution of the nonnegative matrix factorization (NMF). The proposed algorithm can be used for denoising or single-channel source separation (SCSS) applications. The NMF solution is guided to follow th
Externí odkaz:
http://arxiv.org/abs/1302.7283