We investigate a late-fusion approach to piano transcription, combined with a
strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM).
The network architecture under consideration is compact in terms of its number
of parameters and easy to train with gradient descent. The network outputs are
fused over time in the final stage to obtain note segmentations, with an HMM
whose transition probabilities are chosen based on a model of attack, decay,
sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note
segments are then subject to a final binary decision rule to reject too weak
note segment hypotheses. We obtain state-of-the-art results on the MAPS
dataset, and are able to outperform other approaches by a large margin, when
predicting complete note regions from onsets to offsets.

研究了一种迟到融合方法和手工隐藏马尔可夫模型在钢琴转录中的应用，利用 ADSR 包络获取转移概率进行音符分段并经过最终的二进制决策规则，从而实现对完整音符的准确转换。在 MAPS 数据集上取得了最先进的结果，并能够大幅超越其他方法。

深度多音 ADSR 钢琴音符转录

Deep Polyphonic ADSR Piano Note Transcription

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。