From the 1 of 8 linked papers with an AI index.
8 papers
Probing Spatial Structure in Pretrained Audio Representations
Chuyang Chen, Sivan Ding, Adrian S. Roman +1
The paper introduces SARL, a benchmark for evaluating how pretrained audio models encode spatial information such as source direction and room acoustics, and analyzes the strengths…
Sensitivity Analysis of Generative Spatial Audio Metrics: A Study on Responsiveness, Smoothness, and Symmetry
Purnima Kamath, Adrian S. Roman, Koichi Saito +2
Evaluating generative spatial audio for First-Order Ambisonics (FOA) remains challenging due to a limited understanding of how metrics respond to changes in spatial parameters such…
Controllable Embedding Transformation for Mood-Guided Music Retrieval
Julia Wilkins, Jaehun Kim, Matthew E. P. Davies +2
Music representations are the backbone of modern recommendation systems, powering playlist generation, similarity search, and personalized discovery. Yet most embeddings offer litt…
Evaluating Compositional Structure in Audio Representations
Chuyang Chen, Bea Steers, Brian McFee +1
We propose a benchmark for evaluating compositionality in audio representations. Audio compositionality refers to representing sound scenes in terms of constituent sources and attr…
Latent Multi-view Learning for Robust Environmental Sound Representations
Sivan Ding, Julia Wilkins, Magdalena Fuentes +1
Self-supervised learning (SSL) approaches, such as contrastive and generative methods, have advanced environmental sound representation learning using unlabeled data. However, how…
Balancing Information Preservation and Disentanglement in Self-Supervised Music Representation Learning
Julia Wilkins, Sivan Ding, Magdalena Fuentes +1
Recent advances in self-supervised learning (SSL) methods offer a range of strategies for capturing useful representations from music audio without the need for labeled data. While…