Additive manufacturing (AM) is a revolutionary technology that enables production of
intricate structures while minimizing material waste. However, its full potential has yet to be …

Active learning is a label-efficient machine learning method that actively selects the most
valuable unlabeled samples to annotate. Active learning focuses on achieving the best …

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …

DCASE 2017 Challenge consists of four tasks: acoustic scene classification, detection of
rare sound events, sound event detection in real-life audio, and large-scale weakly …

District heat load forecasting is a challenging task that involves predicting future heat
demand based on historical data and various influencing factors. Accurate forecasting is …

As sound event classification moves towards larger datasets, issues of label noise become
inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but …

A typical semi-supervised learning-based scheme is based on training a single model for
labeled data. For unlabeled data, it uses the pseudo-labeling method to obtain labels …

The bird classifier is a system that is equipped with an area machine learning technology
and uses a machine learning method to store and classify bird calls. Bird species can be …

There are lots of research papers for ASC, and in recent years it is rapidly increasing.
DCASE also provides different types of competition for the submission of several papers to …

Few-shot learning has shown promising results in sound event detection where the model
can learn to recognize novel classes assuming a few labeled examples (typically five) are …