The article discusses the limitations of conventional audio classification methods and introduces a novel method that incorporates counterfactual analysis. The proposed model considers acoustic characteristics and sound source information from…
Continue readingThis research introduces CLAPP (Contrastive Language-Audio Pre-training in Passive Underwater Vessel Classification), a novel model for audio classification in passive underwater vessel scenarios. The model uses a neural network trained…
Continue readingThis academic article introduces PosCUDA, a method to create unlearnable audio datasets. The method uses class-wise convolutions on small patches of audio, with the location of these patches based on…
Continue readingThe study titled ‘Multi-label Open-set Audio Classification’ by Sripathi Sridhar and Mark Cartwright discusses the limitations of current audio classification models and the need for open-set audio classification techniques. These…
Continue readingThis research introduces dynamic Convolutional Neural Networks (CNNs) that outperform traditional efficient CNNs and Transformers in terms of the performance complexity trade-off and parameter efficiency. These dynamic CNNs are constructed…
Continue readingThe article is about AUDIO CONFIT, a two-stage contrastive learning-based approach for fine-tuning pre-trained audio models. AUDIO CONFIT aims to strike a balance between fitting the model to the training…
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