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Found 4 papers, 0 papers with code
Masked Autoencoders with Multi-Window Local-Global Attention Are Better Audio Learners
In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows.
Learning neural audio features without supervision
Deep audio classification, traditionally cast as training a deep neural network on top of mel-filterbanks in a supervised fashion, has recently benefited from two independent lines of work.
Revisiting transposed convolutions for interpreting raw waveform sound event recognition CNNs by sonification
The majority of recent work on the interpretability of audio and speech processing deep neural networks (DNNs) interprets spectral information modelled by the first layer, relying solely on visual means of interpretation.
Frequency and temporal convolutional attention for text-independent speaker recognition
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end.
