Multi-pitch estimation is a decades-long research problem involving the
detection of pitch activity associated with concurrent musical events within
multi-instrument mixtures. Supervised learning techniques have demonstrated
solid performance on more narrow characterizations of the task, but suffer from
limitations concerning the shortage of large-scale and diverse polyphonic music
datasets with multi-pitch annotations. We present a suite of self-supervised
learning objectives for multi-pitch estimation, which encourage the
concentration of support around harmonics, invariance to timbral
transformations, and equivariance to geometric transformations. These
objectives are sufficient to train an entirely convolutional autoencoder to
produce multi-pitch salience-grams directly, without any fine-tuning. Despite
training exclusively on a collection of synthetic single-note audio samples,
our fully self-supervised framework generalizes to polyphonic music mixtures,
and achieves performance comparable to supervised models trained on
conventional multi-pitch datasets.

多音高估计中的多音高估计，监督式学习技术，自监督式学习目标，多声乐曲数据集，卷积自编码器

实现全自监督多音高估计

Toward Fully Self-Supervised Multi-Pitch Estimation

State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date,
primarily been carried out using monophonic transcription techniques to handle
complex score layouts, such as polyphony, often by resorting to simplifications
or specific adaptations. Despite their efficacy, these approaches imply
challenges related to scalability and limitations. This paper presents the
Sheet Music Transformer, the first end-to-end OMR model designed to transcribe
complex musical scores without relying solely on monophonic strategies. Our
model employs a Transformer-based image-to-sequence framework that predicts
score transcriptions in a standard digital music encoding format from input
images. Our model has been tested on two polyphonic music datasets and has
proven capable of handling these intricate music structures effectively. The
experimental outcomes not only indicate the competence of the model, but also
show that it is better than the state-of-the-art methods, thus contributing to
advancements in end-to-end OMR transcription.

本研究提出了 Sheet Music Transformer 模型，这是首个能够在处理复杂音乐乐谱时不仅依赖于单音技术的端到端光学乐谱识别模型。模型基于 Transformer 的图像到序列框架，能够从输入图像中预测出标准的数字音乐编码格式的乐谱转录。实验结果表明，该模型不仅表现出了高度的能力，而且也优于目前最先进的方法，从而为端到端光学乐谱识别的进步做出了贡献。