arXiv:2504.18799v1 Announce Type: new
Abstract: Multimodal music emotion recognition (MMER) is an emerging discipline in music information retrieval that has experienced a surge in interest in recent years. This survey provides a comprehensive overview of the current state-of-the-art in MMER. Discussing the different approaches and techniques used in this field, the paper introduces a four-stage MMER framework, including multimodal data selection, feature extraction, feature processing, and final emotion prediction. The survey further reveals significant advancements in deep learning methods and the increasing importance of feature fusion techniques. Despite these advancements, challenges such as the need for large annotated datasets, datasets with more modalities, and real-time processing capabilities remain. This paper also contributes to the field by identifying critical gaps in current research and suggesting potential directions for future research. The gaps underscore the importance of developing robust, scalable, a interpretable models for MMER, with implications for applications in music recommendation systems, therapeutic tools, and entertainment.
Expert Commentary: Multimodal Music Emotion Recognition in the Context of Multimedia Information Systems and Virtual Realities
Music holds great emotional power, and understanding and predicting the emotions it evokes is a fascinating and important area of research. The emerging discipline of Multimodal Music Emotion Recognition (MMER) aims to leverage multiple modalities such as audio, lyrics, gestures, and physiological signals to recognize and predict the emotional content of music. This survey paper provides a comprehensive overview of the current state-of-the-art in MMER, shedding light on the various approaches and techniques used in this field.
The field of MMER intersects with several other domains, making it a truly multi-disciplinary subject. Multimedia Information Systems, for instance, play a significant role in MMER by providing the infrastructure and tools to handle and analyze large volumes of multimodal music data. The techniques discussed in this survey, such as feature extraction and processing, are fundamental to extracting relevant information from music and its associated modalities. These techniques are shared with other fields, such as Speech and Image Processing, highlighting the cross-pollination of knowledge and methodologies.
Furthermore, Animations, Artificial Reality, Augmented Reality, and Virtual Realities are all related to MMER. These technologies offer new ways to experience and interact with music, providing additional modalities for MMER. For example, in Virtual Reality environments, users can be fully immersed in a musical experience and their physiological signals and gestures can be captured, enhancing the multimodal data available for emotion recognition. By incorporating these technologies, MMER can have practical applications in areas such as interactive entertainment, virtual music therapy, and even music recommendation systems that can generate personalized playlists based on the user’s emotional state.
The survey paper highlights the advancements in deep learning methods in MMER. Deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown remarkable performance in various domains, and their application in MMER has yielded promising results. Deep learning allows for the automatic extraction of relevant features from music and other modalities, reducing the need for manual feature engineering. However, it is important to mention that large annotated datasets are still required to train these models effectively, and creating such datasets can be a laborious and resource-intensive task.
The paper also emphasizes the increasing importance of feature fusion techniques in MMER. As the field progresses, researchers are moving towards combining information from multiple modalities to improve emotion recognition accuracy. Fusion techniques such as early fusion, late fusion, and hybrid fusion are discussed in the paper, each with its advantages and trade-offs. The choice of fusion technique depends on the specific requirements of the application and the available data. This trend towards multimodal fusion reflects the realization that a holistic understanding of music emotions requires the integration of information from different sources.
Despite the advancements in MMER, several challenges still need to be addressed. The need for large annotated datasets that cover a wide range of music genres, emotions, and demographic diversity is one significant challenge. Building such datasets is crucial for developing robust and generalizable MMER models. Additionally, the field would benefit from datasets with more modalities, including visual and physiological signals, as they can provide richer information for emotion recognition. Furthermore, real-time processing capabilities are essential for practical applications of MMER, such as interactive music systems. Developing efficient and scalable algorithms to handle real-time multimodal music data is a direction that future research should aim to pursue.
In conclusion, this survey paper provides a comprehensive overview of MMER, its current state-of-the-art, and potential avenues for future research. The multi-disciplinary nature of MMER, with its connections to Multimedia Information Systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities, opens up exciting possibilities for understanding and harnessing the emotional power of music.