Abstract
Music has become an integral part of human life, extending its influence across various industries. For many, music is considered a necessity. With the rise of neural network technology, Music Information Retrieval (MIR) has gained prominence as a multidisciplinary field focused on processing music information and its applications. One popular approach for music captioning is the multimodal encoder-decoder architecture, which utilizes the CNN-LSTM algorithm. In this study, we develop a model that simultaneously learns from audio and text data. We explore different design choices for modality fusion, including early fusion, late fusion, and hybrid fusion, to assess their impact
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
