Audio Datasets

Audio datasets are crucial for training and evaluating machine learning models for various audio-related tasks, ranging from speech recognition to environmental sound classification. Current research focuses on improving dataset quality through techniques like identifying and removing low-quality subsets, developing efficient data distillation methods to reduce storage and computational needs, and adapting model architectures such as transformers and state-space models to handle variable-length audio inputs. These advancements are vital for improving the accuracy and efficiency of audio processing applications across diverse fields, including healthcare, finance, and environmental monitoring.