This paper presents our work to enhance the background music (BGM) in
DareFightingICE by adding an adaptive BGM. The adaptive BGM consists of five
different instruments playing a classical music piece called "Air on G-String."
The BGM adapts by changing the volume of the instruments. Each instrument is
connected to a different element of the game. We then run experiments to
evaluate the adaptive BGM by using a deep reinforcement learning AI that only
uses audio as input (Blind DL AI). The results show that the performance of the
Blind DL AI improves while playing with the adaptive BGM as compared to playing
without the adaptive BGM.

本文提出使用自适应背景音乐（BGM）增强 DareFightingICE 的方法。自适应 BGM 由五个不同的乐器演奏一首古典音乐作品 “G 弦上的爱情” 组成。通过改变各个乐器的音量，实现自适应。运用深度强化学习 AI 进行评估，结果表明使用自适应 BGM 时，Blind DL AI 的表现比不使用自适应 BGM 时有所提升。

适应性格斗游戏背景音乐：一种多乐器音量调制方法

Adaptive Background Music for a Fighting Game: A Multi-Instrument Volume Modulation Approach

This paper introduces a new large-scale music dataset, MusicNet, to serve as
a source of supervision and evaluation of machine learning methods for music
research. MusicNet consists of hundreds of freely-licensed classical music
recordings by 10 composers, written for 11 instruments, together with
instrument/note annotations resulting in over 1 million temporal labels on 34
hours of chamber music performances under various studio and microphone
conditions.
The paper defines a multi-label classification task to predict notes in
musical recordings, along with an evaluation protocol, and benchmarks several
machine learning architectures for this task: i) learning from spectrogram
features; ii) end-to-end learning with a neural net; iii) end-to-end learning
with a convolutional neural net. These experiments show that end-to-end models
trained for note prediction learn frequency selective filters as a low-level
representation of audio.

本文介绍了一个新的大规模音乐数据集 MusicNet，旨在为音乐研究的机器学习方法提供监督和评估，该数据集由 10 位作曲家创作的 11 种乐器的数百个自由许可的古典音乐录音以及关于它们的时间标签组成，其中包含的多标签分类任务以及多项基于机器学习结构的性能评估表明了末端到末端的神经网络在音符预测方面的学习效果最佳。

从头开始学习音乐特征

Learning Features of Music from Scratch

Generative adversarial networks have been proposed as a way of efficiently
training deep generative neural networks. We propose a generative adversarial
model that works on continuous sequential data, and apply it by training it on
a collection of classical music. We conclude that it generates music that
sounds better and better as the model is trained, report statistics on
generated music, and let the reader judge the quality by downloading the
generated songs.

提出了一种基于生成对抗网络的模型，可以高效地训练连续的序列数据，通过在古典音乐集合上训练该模型来生成音乐，发现音乐的质量随着模型的训练不断提高，并提供了生成音乐的统计数据，通过下载生成的歌曲来评估其质量。