Mark D. Plumbley

HCCM's results show that hierarchical clusters analysed by SLINK are interpreted using different classes related to speaker identity, gender, and nationality, providing insight into semantics within the hierarchical organisation of the authors' examined speaker embeddings.

This work applies a hierarchical clustering algorithm to analyse whether representations learned by a speaker recognition network from utterances naturally form hierarchical clusters, and proposes a new task, second-order pattern recognition, to identify which discovered second-order patterns characterising known utterances are exhibited by an unseen utterance.

Audio-language models struggle significantly with hidden evaluation tasks, with accuracy plummeting by nearly 12 percentage points on average, highlighting the challenge of true audio comprehension.

Pruning can slash the computational cost of text-to-audio models by over 80% without sacrificing quality, but it poses risks to generating critical sound events.

Adapting speech enhancement models can significantly boost singing voice separation performance without sacrificing their original capabilities.