arXiv:2408.16990v1 Announce Type: new
Abstract: Adding proper background music helps complete a short video to be shared. Towards automating the task, previous research focuses on video-to-music retrieval (VMR), aiming to find amidst a collection of music the one best matching the content of a given video. Since music tracks are typically much longer than short videos, meaning the returned music has to be cut to a shorter moment, there is a clear gap between the practical need and VMR. In order to bridge the gap, we propose in this paper video to music moment retrieval (VMMR) as a new task. To tackle the new task, we build a comprehensive dataset Ad-Moment which contains 50K short videos annotated with music moments and develop a two-stage approach. In particular, given a test video, the most similar music is retrieved from a given collection. Then, a Transformer based music moment localization is performed. We term this approach Retrieval and Localization (ReaL). Extensive experiments on real-world datasets verify the effectiveness of the proposed method for VMMR.
Automating Video to Music Moment Retrieval: Bridging the Gap
In the field of multimedia information systems, the integration of audio and visual elements is crucial for creating immersive experiences. One key aspect of this integration is the synchronization of background music with video content. Adding proper background music not only enhances the emotional impact of a video but also helps to engage and captivate the audience.
Previous research has primarily focused on video-to-music retrieval (VMR), which aims to find the best-matching music track for a given video from a collection of music tracks. However, a significant gap exists between the practical need, where short videos need to be matched with shorter music moments, and the capabilities of VMR systems.
Addressing this gap, the authors propose a new task called video to music moment retrieval (VMMR). This task involves retrieving the most similar music moment from a given collection for a given test video. To support the development and evaluation of VMMR algorithms, the authors introduce the Ad-Moment dataset, which includes 50,000 short videos annotated with music moments.
The authors propose a two-stage approach, named Retrieval and Localization (ReaL), to tackle the VMMR task. In the first stage, the most similar music track is retrieved from the collection using a similarity measure. In the second stage, a Transformer-based model is employed to perform music moment localization, i.e., identifying the specific portion of the retrieved music track that best matches the video.
Multiple disciplines intersect in this research, highlighting the multi-disciplinary nature of multimedia information systems. The study combines concepts from computer vision, audio processing, machine learning, and artificial intelligence to automate the process of video to music moment retrieval.
Furthermore, the proposed method has implications for other areas such as animations, artificial reality, augmented reality, and virtual realities. These fields often rely on multimedia content to create immersive and engaging experiences. By automating the process of matching music moments with video content, the proposed method can enhance the creation of animations, improve the realism of artificial and augmented reality environments, and enrich the immersion of virtual reality experiences.
The effectiveness of the proposed method for VMMR is verified through extensive experiments on real-world datasets. The results demonstrate the potential of the approach to bridge the gap between practical needs and existing VMR capabilities. As future work, further refinements and optimizations of the ReaL approach could be explored, such as incorporating user preferences, evaluating the impact of different music genres on video engagement, and exploring novel methods for music moment retrieval and localization.