88 citations · 100 across the 7 of their papers we have counts for
9 papers
Direct Preference Density Alignment for Conversational Audio Equalization
Ioannis Stylianou, Sven Ewan Shepstone, Jon Francombe +2
Large Language Model alignment typically relies on learned proxy reward models, which significantly increase the memory footprint during training and are notoriously prone to insta…
One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization
Ioannis Stylianou, Jon Francombe, Pablo Martinez-Nuevo +2
Conventional audio equalization is a static process that requires manual and cumbersome adjustments to adapt to changing listening contexts (e.g., mood, location, or social setting…
Data Aware Differentiable Neural Architecture Search for Tiny Keyword Spotting Applications
Yujia Shi, Emil Njor, Pablo Martínez-Nuevo +2
The success of Machine Learning is increasingly tempered by its significant resource footprint, driving interest in efficient paradigms like TinyML. However, the inherent complexit…
Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content
Miklas S. Kristoffersen, Sven E. Shepstone, Zheng-Hua Tan
This paper studies context-aware recommendations in the television domain by proposing a deep learning-based method for learning joint context-content embeddings (JCCE). The method…
Sound field reconstruction in rooms: inpainting meets super-resolution
Francesc Lluís, Pablo Martínez-Nuevo, Martin Bo Møller +1
In this paper, a deep-learning-based method for sound field reconstruction is proposed. It is shown the possibility to reconstruct the magnitude of the sound pressure in the freque…
Deep Joint Embeddings of Context and Content for Recommendation
Miklas S. Kristoffersen, Jacob L. Wieland, Sven E. Shepstone +2
This paper proposes a deep learning-based method for learning joint context-content embeddings (JCCE) with a view to context-aware recommendations, and demonstrate its application…