PopMAG：流行音乐伴奏一代
PopMAG: Pop Music Accompaniment Generation
摘要：
在流行音乐中，伴奏通常由多种乐器（音轨）演奏，如鼓，贝司，弦乐和吉他，并可以通过与旋律一起安排使歌曲更具表现力和感染力。以往的作品通常是分别生成多个音轨，不同音轨的音符之间没有明确的依赖关系，不利于和声建模。为了提高和谐，在本文中，我们提出了一种新的多轨道表示（Muximity），它使同时多轨道生成在一个单一的序列和明确的模型，从不同的轨道的音符的依赖性。虽然这极大地改善了和声，但不幸的是，它增加了序列长度，并带来了长期音乐建模的新挑战。我们进一步引入两种新技术来解决这一挑战：1）我们对多个音符属性（例如，音高、持续时间、速度），这可以缩短Muffel序列的长度。2)我们引入额外的长期背景作为记忆，以捕捉音乐中的长期依赖性。我们把我们的流行音乐伴奏生成系统称为PopMAG。我们在多个数据集（LMD，FreeMidi和CPMD，中国流行歌曲的私人数据集）上使用主观和客观指标评估PopMAG。实验结果表明PopMAG在多音轨和声建模和长期背景建模方面的有效性。具体来说，PopMAG在LMD、FreeMidi和CPMD数据集上分别获得了42%/38%/40%的选票，并且在主观和客观指标方面大大优于其他最先进的音乐伴奏生成模型和多音轨伴奏表示。
英文摘要：
In pop music, accompaniments are usually played by multiple instruments (tracks) such as drum, bass, string and guitar, and can make a song more expressive and contagious by arranging together with its melody. Previous works usually generate multiple tracks separately and the music notes from different tracks not explicitly depend on each other, which hurts the harmony modeling. To improve harmony, in this paper, we propose a novel MUlti-track MIDI representation (MuMIDI), which enables simultaneous multi-track generation in a single sequence and explicitly models the dependency of the notes from different tracks. While this greatly improves harmony, unfortunately, it enlarges the sequence length and brings the new challenge of long-term music modeling. We further introduce two new techniques to address this challenge: 1) We model multiple note attributes (e.g., pitch, duration, velocity) of a musical note in one step instead of multiple steps, which can shorten the length of a MuMIDI sequence. 2) We introduce extra long-context as memory to capture long-term dependency in music. We call our system for pop music accompaniment generation as PopMAG. We evaluate PopMAG on multiple datasets (LMD, FreeMidi and CPMD, a private dataset of Chinese pop songs) with both subjective and objective metrics. The results demonstrate the effectiveness of PopMAG for multi-track harmony modeling and long-term context modeling. Specifically, PopMAG wins 42\%/38\%/40\% votes when comparing with ground truth musical pieces on LMD, FreeMidi and CPMD datasets respectively and largely outperforms other state-of-the-art music accompaniment generation models and multi-track MIDI representations in terms of subjective and objective metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
