Multi-instrument Automatic Music Transcription (AMT), or the decoding of a
musical recording into semantic musical content, is one of the holy grails of
Music Information Retrieval. Current AMT approaches are restricted to piano and
(some) guitar recordings, due to difficult data collection. In order to
overcome data collection barriers, previous AMT approaches attempt to employ
musical scores in the form of a digitized version of the same song or piece.
The scores are typically aligned using audio features and strenuous human
intervention to generate training labels. We introduce NoteEM, a method for
simultaneously training a transcriber and aligning the scores to their
corresponding performances, in a fully-automated process. Using this unaligned
supervision scheme, complemented by pseudo-labels and pitch-shift augmentation,
our method enables training on in-the-wild recordings with unprecedented
accuracy and instrumental variety. Using only synthetic data and unaligned
supervision, we report SOTA note-level accuracy of the MAPS dataset, and large
favorable margins on cross-dataset evaluations. We also demonstrate robustness
and ease of use; we report comparable results when training on a small, easily
obtainable, self-collected dataset, and we propose alternative labeling to the
MusicNet dataset, which we show to be more accurate. Our project page is
available at this https URL

NoteEM 是一种自动化的乐曲信息获取方法，能够实现从现实世界的音频记录中以高精度解码出对应的乐曲内容，其主要特点为采用了非对齐监督、伪标签和音高位移等技术。该方法在 MAPS 数据集上取得了最好的记录水平准确度，并在跨数据集评估中也表现出积极优势。

野外音乐自动转录的非对齐监督

Unaligned Supervision For Automatic Music Transcription in The Wild

In an attempt at exploring the limitations of simple approaches to the task
of piano transcription (as usually defined in MIR), we conduct an in-depth
analysis of neural network-based framewise transcription. We systematically
compare different popular input representations for transcription systems to
determine the ones most suitable for use with neural networks. Exploiting
recent advances in training techniques and new regularizers, and taking into
account hyper-parameter tuning, we show that it is possible, by simple
bottom-up frame-wise processing, to obtain a piano transcriber that outperforms
the current published state of the art on the publicly available MAPS dataset
-- without any complex post-processing steps. Thus, we propose this simple
approach as a new baseline for this dataset, for future transcription research
to build on and improve.

在钢琴转录任务中，采用基于神经网络的帧级别方法，并通过系统比较适用于神经网络的不同流行的输入表示方法并充分利用训练技术和新的正则化方法，得出了一个比目前公开发表的最新技术在 MAPS 数据集上性能更好的钢琴转录器。因此，本文提出这种简单的方法作为未来音乐转录研究的新基准。