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Found 4 papers, 3 papers with code
LiMuSE: Lightweight Multi-modal Speaker Extraction
Multi-modal cues, including spatial information, facial expression and voiceprint, are introduced to the speech separation and speaker extraction tasks to serve as complementary information to achieve better performance.
WASE: Learning When to Attend for Speaker Extraction in Cocktail Party Environments
In the speaker extraction problem, it is found that additional information from the target speaker contributes to the tracking and extraction of the target speaker, which includes voiceprint, lip movement, facial expression, and spatial information.
Audio-visual Speech Separation with Adversarially Disentangled Visual Representation
In our model, we use the face detector to detect the number of speakers in the scene and use visual information to avoid the permutation problem.
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset).
