Music auto-tagging is key for improving music discovery and recommendation. Existing models in Music Information Retrieval (MIR) struggle with real-world noise. This study proposes a method that integrates Domain Adversarial…
Continue readingThe paper proposes a DeepJointCascade Model (DJCM) for singing voice separation and vocal pitch estimation tasks in music information retrieval. Traditional methods, classified into pipeline methods and naive joint learning…
Continue readingThe research presents a novel approach to enhance the understanding of tabular data extracted from PDFs for efficient summarization by language models. The approach involves storing PDFs in the retrieval…
Continue readingThis article describes a new method for generating music symbolically, using a graph that represents the structure of a song and a Graph Neural Network. The graph uses information such…
Continue readingThe article presents BEAST (BEA t tracking Streaming Transformer), a system for online beat and downbeat tracking. The system, which is based on the streaming Transformer, is designed to handle…
Continue readingThe article presents an in-depth study of foundation models tailored for music informatics, a domain challenged by the scarcity of labeled data and generalization issues. The researchers conducted a comparative…
Continue readingThis article discusses the use of semi-supervised learning and a teacher-student training approach to improve Music Information Retrieval (MIR) tasks. The authors scaled up unlabeled music data to 240k hours…
Continue readingThe article ‘Towards Robust and Truly Large-Scale Audio Sheet Music Retrieval’ discusses the application of deep learning in connecting large collections of sheet music to corresponding audio recordings. It identifies…
Continue readingThis academic article discusses a novel approach to multi-instrument music synthesis using performance conditioning. The authors propose a generative model that is conditioned on a specific performance and recording environment,…
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