用哼唱匹配歌曲，机器识别准确率近94%。
An approach to hummed-tune and song sequences matching
- 将哼唱音频转为可训练特征，用嵌入模型提取音调序列
- 在公开测试集上达到MRR@10 94%的匹配准确率
- 适合音乐检索、智能语音助手等实际应用场景
脑海中反复回响的旋律（耳虫）令人烦躁，若无法找到歌曲名称则更添困扰。通过哼唱识别歌曲名称对人类困难，需由机器完成。目前尚无相关研究发表。本文基于2021年Hum2Song Zalo AI挑战赛，将原始mp3音频预处理为可用于训练和推理的格式。在特征提取阶段，实验了ResNet、VGG、AlexNet、MobileNetV2等主流模型；推理阶段采用Faiss模块高效搜索与哼唱序列匹配的歌曲。在公开测试集上，MRR@10指标达94%，并获得公开排行榜第一名。
原文摘要 · Abstract (English)
Melody stuck in your head, also known as "earworm", is tough to get rid of, unless you listen to it again or sing it out loud. But what if you can not find the name of that song? It must be an intolerable feeling. Recognizing a song name base on humming sound is not an easy task for a human being and should be done by machines. However, there is no research paper published about hum tune recognition. Adapting from Hum2Song Zalo AI Challenge 2021 - a competition about querying the name of a song by user's giving humming tune, which is similar to Google's Hum to Search. This paper covers details about the pre-processed data from the original type (mp3) to usable form for training and inference. In training an embedding model for the feature extraction phase, we ran experiments with some states of the art, such as ResNet, VGG, AlexNet, MobileNetV2. And for the inference phase, we use the Faiss module to effectively search for a song that matched the sequence of humming sound. The result comes at nearly 94\% in MRR@10 metric on the public test set, along with the top 1 result on the public leaderboard.
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