3 accepted papers
This paper introduces GraFPrint, an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio fingerprints. Our method constructs a k-nearest neighbour (k-NN) graph from time-frequency representations and applies max-rel…
Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to process the higher-order relations essential for identifying dist…
Acoustic Scene Classification (ASC) refers to the task of assigning a semantic label to an audio stream that characterizes the environment in which it was recorded. In recent times, Deep Neural Networks (DNNs) have emerged as the model of choice for ASC. However, in real world scenarios, domain adap…