Supervised learning for audio classification typically imposes a fixed class vocabulary,
which can be limiting for real-world applications where the target class vocabulary is not …

Most existing methods for audio classification assume that the vocabulary of audio classes
to be classified is fixed. When novel (unseen) audio classes appear, audio classification …

It is generally assumed that number of classes is fixed in current audio classification
methods, and the model can recognize pregiven classes only. When new classes emerge …

Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of
stationary and non-stationary sounds from various events, background noises and human …

This work explores class-incremental learning (CIL) for sound event detection (SED),
advancing adaptability towards real-world scenarios. CIL's success in domains like …

Convolutional neural network suffers from catastrophic forgetting during continual learning.
This is one of the major obstacles for artificial intelligence, to solve new problems without …

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis
approaches assume that the data distribution at training and deployment time will be the …

Sound event detection (SED) refers to recognizing the sound events in a continuous audio
signal, which has drawn increasing interest during recent decades. The applications of SED …

This article proposes an efficient audio incremental learning method to reduce the
computational complexity and catastrophic forgetting during the incremental addition of the …

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 …