We present an automatic piano transcription system that converts polyphonic
audio recordings into musical scores. This has been a long-standing problem of
music information processing, and recent studies have made remarkable progress
in the two main component techniques: multipitch detection and rhythm
quantization. Given this situation, we study a method integrating
deep-neural-network-based multipitch detection and statistical-model-based
rhythm quantization. In the first part, we conducted systematic evaluations and
found that while the present method achieved high transcription accuracies at
the note level, some global characteristics of music, such as tempo scale,
metre (time signature), and bar line positions, were often incorrectly
estimated. In the second part, we formulated non-local statistics of pitch and
rhythmic contents that are derived from musical knowledge and studied their
effects in inferring those global characteristics. We found that these
statistics are markedly effective for improving the transcription results and
that their optimal combination includes statistics obtained from separated hand
parts. The integrated method had an overall transcription error rate of 7.1%
and a downbeat F-measure of 85.6% on a dataset of popular piano music, and the
generated transcriptions can be partially used for music performance and
assisting human transcribers, thus demonstrating the potential for practical
applications.

提出了一种自动钢琴转录系统，该系统利用基于深度神经网络的多音高检测和基于统计模型的节奏量化相结合的方法提高了音乐信息处理的准确性，在系统性评估中发现一些全局特征误差较大，而由音乐知识推导的音高和节奏内容的非局部统计显著提高了转录结果的准确性。

音乐非局部统计作为音频转谱钢琴转录的指导

Non-Local Musical Statistics as Guides for Audio-to-Score Piano  Transcription

Recent advances in neural autoregressive models have improve the performance
of speech synthesis (SS). However, as they lack the ability to model global
characteristics of speech (such as speaker individualities or speaking styles),
particularly when these characteristics have not been labeled, making neural
autoregressive SS systems more expressive is still an open issue. In this
paper, we propose to combine VoiceLoop, an autoregressive SS model, with
Variational Autoencoder (VAE). This approach, unlike traditional autoregressive
SS systems, uses VAE to model the global characteristics explicitly, enabling
the expressiveness of the synthesized speech to be controlled in an
unsupervised manner. Experiments using the VCTK and Blizzard2012 datasets show
the VAE helps VoiceLoop to generate higher quality speech and to control the
expressions in its synthesized speech by incorporating global characteristics
into the speech generating process.

本文提出一种将自回归语音合成模型 VoiceLoop 与变分自编码器 VAE 相结合的方法，通过在语音生成过程中显式建模全局特征，控制生成语音的表达方式从而提高语音合成的表现力。