arXiv:2504.12796v1 Announce Type: new
Abstract: Multimodal learning has driven innovation across various industries, particularly in the field of music. By enabling more intuitive interaction experiences and enhancing immersion, it not only lowers the entry barriers to the music but also increases its overall appeal. This survey aims to provide a comprehensive review of multimodal tasks related to music, outlining how music contributes to multimodal learning and offering insights for researchers seeking to expand the boundaries of computational music. Unlike text and images, which are often semantically or visually intuitive, music primarily interacts with humans through auditory perception, making its data representation inherently less intuitive. Therefore, this paper first introduces the representations of music and provides an overview of music datasets. Subsequently, we categorize cross-modal interactions between music and multimodal data into three types: music-driven cross-modal interactions, music-oriented cross-modal interactions, and bidirectional music cross-modal interactions. For each category, we systematically trace the development of relevant sub-tasks, analyze existing limitations, and discuss emerging trends. Furthermore, we provide a comprehensive summary of datasets and evaluation metrics used in multimodal tasks related to music, offering benchmark references for future research. Finally, we discuss the current challenges in cross-modal interactions involving music and propose potential directions for future research.
The Role of Multimodal Learning in Music: A Comprehensive Review
In recent years, there has been a significant focus on multimodal learning, particularly in the field of music. This approach, which combines multiple modes of communication and interaction, has led to innovation that not only enhances the music experience but also breaks down barriers to entry for aspiring musicians. In this survey, we aim to provide a comprehensive review of multimodal tasks related to music, exploring the ways in which music contributes to multimodal learning and offering insights for researchers looking to push the boundaries of computational music.
Unlike text and images, which can be easily understood through semantics and visualization, music primarily relies on auditory perception for its interaction with humans. This inherent less intuitive nature of music data representation poses challenges for researchers and developers working on multimodal tasks. Therefore, this paper begins by discussing the various representations of music and providing an overview of music datasets. By understanding the unique characteristics of music, researchers can better design multimodal systems that effectively integrate with music.
Categorizing Cross-Modal Interactions in Music
The survey goes on to categorize cross-modal interactions between music and multimodal data into three types:
- Music-driven cross-modal interactions: This category explores the ways in which music affects and drives other modalities, such as visuals or haptic feedback. For example, in a music video, the visuals are often synchronized with the rhythm and mood of the music, enhancing the overall cinematic experience. Understanding these interactions between music and other modalities can lead to more immersive multimedia experiences.
- Music-oriented cross-modal interactions: Here, the focus is on how other modalities, such as visual cues or gestures, can influence and shape the production or performance of music. For instance, a musician may use a gesture recognition system to control specific musical parameters in real-time. By studying these interactions, researchers can develop new tools and techniques for musical expression and performance.
- Bidirectional music cross-modal interactions: This category involves exploring the reciprocal and bidirectional relationships between music and other modalities. It delves into how music can influence other modalities and vice versa, creating a dynamic and interactive multimodal experience. For example, in virtual reality (VR) environments, music can adapt and respond to the user’s actions, creating a more responsive and engaging experience.
By systematically tracing the development of relevant sub-tasks within each category, analyzing existing limitations, and discussing emerging trends, this survey provides a comprehensive understanding of the current state of multimodal tasks related to music. It serves as a valuable resource for researchers and developers interested in exploring new avenues in computational music.
Relevant to the Field of Multimedia Information Systems
Within the wider field of multimedia information systems, this survey holds great significance. The fusion of different modalities and the integration of music into multimodal learning have the potential to revolutionize how we interact with and consume multimedia content. By understanding the cross-modal interactions in music, researchers can develop more sophisticated multimedia systems that cater to personalized preferences and enhance user engagement.
Linking with Animations, Artificial Reality, Augmented Reality, and Virtual Realities
This survey also sheds light on the interconnectedness between music and various visualization technologies, such as animations, artificial reality, augmented reality, and virtual realities. By leveraging cross-modal interactions, these technologies can provide a more immersive and captivating experience. For example, in virtual reality, music can be synchronized with visual cues to create a truly immersive environment. Similarly, in augmented reality, music-driven interactions can enhance the overall user experience.
As the boundaries of computational music continue to expand, it is crucial for researchers to consider the multidisciplinary nature of the concepts discussed in this survey. The integration of music with multimodal learning, animations, artificial reality, augmented reality, and virtual realities opens up countless opportunities for creative expression, entertainment, and even therapeutic applications.
Conclusion: Challenges and Future Directions
This survey concludes by discussing the current challenges in cross-modal interactions involving music and proposing potential directions for future research. Some of the key challenges include improving the semantic understanding of music, enhancing the synchronization between music and other modalities, and addressing the limitations of current evaluation metrics. Additionally, researchers are encouraged to explore novel applications of music-driven cross-modal interactions in areas such as healthcare, education, and gaming.
In summary, this comprehensive review of multimodal tasks related to music provides a valuable resource for researchers and developers in the field of computational music. By understanding the multidisciplinary nature of these tasks and their relevance to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, we can unlock new possibilities for music-related experiences and pave the way for future advancements in this exciting area of research.