We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a
contrastive learning method for musical audio representations that captures the
harmonic and rhythmic coherence between samples. Our method operates at the
level of stems (or their combinations) composing music tracks and allows the
objective evaluation of compositional models for music in the task of
accompaniment generation. We also introduce a new baseline for compositional
music generation called CompoNet, based on ControlNet \cite{zhang2023adding},
generalizing the tasks of MSDM, and quantify it against the latter using
COCOLA. We release all models trained on public datasets containing separate
stems (MUSDB18-HQ, MoisesDB, Slakh2100, and CocoChorales).

COCOLA 是一种用于音乐音频表示的对比学习方法，捕捉样本之间的和谐和节奏的一致性，目的在于对音乐作品的组合模型进行伴奏生成的客观评估。我们还引入了基于 ControlNet 的音乐组合生成新基准 CompoNet，并使用 COCOLA 与 ControlNet 进行量化比较。我们公开发布了在包含不同音轨的公共数据集（MUSDB18-HQ、MoisesDB、Slakh2100 和 CocoChorales）上训练的所有模型。

COCOLA：面向一致性的音乐音频对比学习

COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio  Representations

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.

本文提出了一种新的多音轨 MIDI 表示（MuMIDI），并引入了额外的长期上下文作为记忆以捕捉音乐中的长期依赖性，通过在多个数据集上进行主观和客观指标的比较，证明了其在流行音乐伴奏生成和长期上下文建模方面的有效性。