2 citations · 4 across the 4 of their papers we have counts for
4 papers
Device-Robust Acoustic Scene Classification via Impulse Response Augmentation
Tobias Morocutti, Florian Schmid, Khaled Koutini +1
The ability to generalize to a wide range of recording devices is a crucial performance factor for audio classification models. The characteristics of different types of microphone…
Low-Complexity Audio Embedding Extractors
Florian Schmid, Khaled Koutini, Gerhard Widmer
Solving tasks such as speaker recognition, music classification, or semantic audio event tagging with deep learning models typically requires computationally demanding networks. Ge…
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers
Khaled Koutini, Shahed Masoudian, Florian Schmid +3
The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale dataset…
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
Florian Schmid, Khaled Koutini, Gerhard Widmer
Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the abili…