We propose a method to recommend music for an input video while allowing a
user to guide music selection with free-form natural language. A key challenge
of this problem setting is that existing music video datasets provide the
needed (video, music) training pairs, but lack text descriptions of the music.
This work addresses this challenge with the following three contributions.
First, we propose a text-synthesis approach that relies on an analogy-based
prompting procedure to generate natural language music descriptions from a
large-scale language model (BLOOM-176B) given pre-trained music tagger outputs
and a small number of human text descriptions. Second, we use these synthesized
music descriptions to train a new trimodal model, which fuses text and video
input representations to query music samples. For training, we introduce a text
dropout regularization mechanism which we show is critical to model
performance. Our model design allows for the retrieved music audio to agree
with the two input modalities by matching visual style depicted in the video
and musical genre, mood, or instrumentation described in the natural language
query. Third, to evaluate our approach, we collect a testing dataset for our
problem by annotating a subset of 4k clips from the YT8M-MusicVideo dataset
with natural language music descriptions which we make publicly available. We
show that our approach can match or exceed the performance of prior methods on
video-to-music retrieval while significantly improving retrieval accuracy when
using text guidance.

该研究提出了一种音乐推荐的方法，允许用户使用自由形式的自然语言来引导音乐选择，解决了没有音乐文本描述的挑战，并使用文本合成方法训练了新的三模态模型，该模型的检索音频可通过与视频呈现的视觉风格和语言查询中描述的音乐风格、心情或器乐匹配，以匹配两个输入模态的准确性。