Dynamic Convolutional Neural Networks as Efficient Pre-Trained Audio Models
Florian Schmid,
Khaled Koutini,
Gerhard Widmer
Abstract:The introduction of large-scale audio datasets, such as AudioSet, paved the way for Transformers to conquer the audio domain and replace CNNs as the state-of-the-art neural network architecture for many tasks. Audio Spectrogram Transformers are excellent at exploiting large datasets, creating powerful pre-trained models that surpass CNNs when fine-tuned on downstream tasks. However, current popular Audio Spectrogram Transformers are demanding in terms of computational complexity compared to CNNs. Recently, we … Show more
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