Shot Class Incremental Audio Classification

Shot class incremental audio classification focuses on building machine learning models that can efficiently learn to classify new audio classes with limited training data, while retaining the ability to recognize previously learned classes. Current research emphasizes techniques like meta-learning, prototype-based methods, and the adaptation of large language models to audio via novel codecs, aiming to improve accuracy and reduce the catastrophic forgetting of previously learned sounds. This field is crucial for developing robust and adaptable audio recognition systems in real-world scenarios where continuously updating models with scarce data is necessary, impacting applications such as environmental monitoring and personalized audio assistants.