It is a well-established practice to build a robust system for sound event detection by training
supervised deep learning models on large datasets, but audio data collection and labeling …

New classes of sounds constantly emerge with a few samples, making it challenging for
models to adapt to dynamic acoustic environments. This challenge motivates us to address …

Few-shot Class-incremental Audio Classification (FCAC) is a task to continuously identify
incremental classes with only few training samples after training the model on base classes …

It's assumed that training data is sufficient in base session of few-shot class-incremental
audio classification. However, it's difficult to collect abundant samples for model training in …

Plant diseases are one of the main causes of crop loss in agriculture. Machine Learning, in
particular statistical and neural nets (NNs) approaches, have been used to help farmers …

Unlike the field of visual scene recognition where tremendous advances have taken place
due to the availability of very large datasets to train deep neural networks, inference from …

Il mondo naturale è ricco di conversazioni, molte delle quali fuori dell'intervallo di udibilità
dell'orecchio umano. Oggi però gli scienziati utilizzano strumenti digitali all'avanguardia per …

Several challenges arise in the application of modern computer vision and machine learning
techniques to making inferences from medical images. They include relatively low sample …

Abstract Inductive Logic Programming (ILP), a form of machine learning, stands out due to its
emphasis on explainability and its symbolic representation. ILP combines logic …

Nowadays deep learning-based approaches have become popular tools and achieved
promising results in machine listening. A deep model that generalizes well needs to be …