2 citations · 10 across the 21 of their papers we have counts for
5 papers · 1 filter
Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
Kang Zhang, Trung X. Pham, Suyeon Lee +3
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike…
ElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions
Jiu Feng, Mehmet Hamza Erol, Joon Son Chung +1
Transformers have rapidly overtaken CNN-based architectures as the new standard in audio classification. Transformer-based models, such as the Audio Spectrogram Transformers (AST),…
Audio Mamba: Bidirectional State Space Model for Audio Representation Learning
Mehmet Hamza Erol, Arda Senocak, Jiu Feng +1
Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs. However, Audio Spectrogram Transformers (ASTs) exhibit quadratic s…
From Coarse to Fine: Efficient Training for Audio Spectrogram Transformers
Jiu Feng, Mehmet Hamza Erol, Joon Son Chung +1
Transformers have become central to recent advances in audio classification. However, training an audio spectrogram transformer, e.g. AST, from scratch can be resource and time-int…
FlexiAST: Flexibility is What AST Needs
Jiu Feng, Mehmet Hamza Erol, Joon Son Chung +1
The objective of this work is to give patch-size flexibility to Audio Spectrogram Transformers (AST). Recent advancements in ASTs have shown superior performance in various audio-b…