论文标题
半监督的卷曲NMF自动钢琴转录
Semi-Supervised Convolutive NMF for Automatic Piano Transcription
论文作者
论文摘要
自动音乐转录包括将音乐表演的音频录制转换为符号格式，这仍然是一项困难的音乐信息检索任务。在重点介绍钢琴转录的这项工作中，我们提出了使用低级别基质分解技术的半监督方法，特别是备受推验的非负矩阵分解。在半监督的设置中，只需要单个音符的单个记录。我们在地图数据集上显示，提议的半监督CNMF方法的性能优于最先进的低级分解技术，并且比监督的深度学习最新方法更差，但是却遭受了概括性问题的困扰。
Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task. In this work, which focuses on piano transcription, we propose a semi-supervised approach using low-rank matrix factorization techniques, in particular Convolutive Nonnegative Matrix Factorization. In the semi-supervised setting, only a single recording of each individual notes is required. We show on the MAPS dataset that the proposed semi-supervised CNMF method performs better than state-of-the-art low-rank factorization techniques and a little worse than supervised deep learning state-of-the-art methods, while however suffering from generalization issues.
