Search Results for author:
Found 19 papers, 3 papers with code
TA-V2A: Textually Assisted Video-to-Audio Generation
As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation.
Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment
One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93. 1% in 1-second decoding window.
Cross-attention Inspired Selective State Space Models for Target Sound Extraction
In this paper, we propose CrossMamba for target sound extraction, which leverages the hidden attention mechanism of Mamba to compute dependencies between the given clues and the audio mixture.
Leveraging Moving Sound Source Trajectories for Universal Sound Separation
This paper proposes a method utilizing the mutual facilitation mechanism between sound source localization and separation for moving sources.
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they argued that such decoding accuracy was overestimated due to the inherent temporal autocorrelation of EEG signals.
ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Contrastive learning was used to relate EEG features to speech features.
A DenseNet-based method for decoding auditory spatial attention with EEG
ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings.
Direct source and early reflections localization using deep deconvolution network under reverberant environment
This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberant scenarios.
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network
In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models.
Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks
These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.
Embodied Self-supervised Learning by Coordinated Sampling and Training
The proposed approach works in an analysis-by-synthesis manner to learn an inference network by iteratively sampling and training.
Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition
Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history.
Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks.
