Music Classification
Music classification, the task of automatically categorizing music into genres, subgenres, or eras, aims to improve music information retrieval and enhance user experiences in applications like music recommendation and playlist generation. Current research focuses on developing robust models, often employing deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, and exploring diverse audio representations beyond traditional spectrograms, including those informed by generative models and incorporating multimodal data like lyrics or artist information. These advancements are significant for both the scientific community, pushing the boundaries of audio signal processing and machine learning, and for practical applications, leading to more effective and personalized music experiences.
Papers
Dynamic Range Compression and Its Effect on Music Genre Classification
Arlyn Reese Madsen III
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Shangda Wu, Yashan Wang, Ruibin Yuan, Zhancheng Guo, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun