7 papers
COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Ruike Cao, Fugen Yao, Liang Dong +3
While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in homogenized responses that fai…
Misrecognition or Abstraction? Rethinking Outputs of Sound Event Recognition
Naoya Tomida, Yuki Okamoto, Keisuke Imoto
Conventional general sound recognition systems typically output deterministic sound event labels, implicitly assuming that the target sound class can be correctly identified from t…
Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation
Yizhou Zhang, Wangjin Zhou, Yi Zhao +3
Music aesthetics scoring plays a critical role in applications such as dataset curation, generative model evaluation, and reward modeling for music generation. Recent approaches re…
Sound Scene Synthesis at the DCASE 2024 Challenge
Mathieu Lagrange, Junwon Lee, Modan Tailleur +5
This paper presents Task 7 at the DCASE 2024 Challenge: sound scene synthesis. Recent advances in sound synthesis and generative models have enabled the creation of realistic and d…
Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation
Junwon Lee, Modan Tailleur, Laurie M. Heller +5
Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene…
Construction and Analysis of Impression Caption Dataset for Environmental Sounds
Yuki Okamoto, Ryotaro Nagase, Minami Okamoto +4
Some datasets with the described content and order of occurrence of sounds have been released for conversion between environmental sound and text. However, there are very few texts…
Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
Modan Tailleur, Junwon Lee, Mathieu Lagrange +4
This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet Audio Distance (FAD) metric can help the FAD to correlate better with percep…