Learning Tonal Representations of Music Signals Using Deep Neural Networks

使用深度神经网络学习音乐信号的音调表示

• 批准号: 443992185
• 负责人: Professor Dr.-Ing. Christof Weiß
• 金额: --
• 依托单位: LInstitut de Recherche et Coordination Acoustique-Musique
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/443992185?language=en
• 关键词: Learning; Tonal; Representations; Music; Signals

With the growing impact of technology, musicological research is subject to a fundamental transformation. Digitized data and specialized algorithms enable systematic analyses of large music corpora. Recently, such corpus studies were performed based on audio recordings involving methods from digital signal processing and machine learning. In this context, the tonal analysis of the music signals regarding chords, scales, or keys plays a significant role. Traditional analysis methods rely on signal processing techniques to extract tonal feature representations that indicate the presence of musical pitch classes over time, thus allowing for an explicit semantic interpretation. The objective of this project is to use deep neural networks for learning tonal representations, which are interpretable, robust, and invariant regarding timbre, instrumentation, and acoustic conditions. The project builds on complex scenarios of classical music where time-aligned scores and multiple performances of the pieces can be used for training, validating, and testing the algorithms. From a technical perspective, this project investigates approaches for learning pitch-class, multi-pitch, and salience representations. Among others, sequence learning techniques that can handle weakly-aligned annotations and U-net architectures that are inspired by hierarchical musical structures will be explored. Applying the learned representations to complex music scenarios aims for developing robust tonal analysis methods by exploiting the potential of novel deep-learning algorithms, thus paving the way towards a new level of computational music research.

随着技术影响力的不断增强，音乐学研究正在发生根本性的转变。数字化数据和专门的算法可以对大型音乐语料库进行系统分析。最近，此类语料库研究是基于涉及数字信号处理和机器学习方法的录音进行的。在这种情况下，关于和弦、音阶或调的音乐信号的音调分析起着重要作用。传统的分析方法依赖于信号处理技术来提取音调特征表示，这些特征表示表明随着时间的推移音乐音高类别的存在，从而允许明确的语义解释。该项目的目标是使用深度神经网络来学习音调表示，这些表示在音色、乐器和声学条件方面具有可解释性、鲁棒性和不变性。该项目建立在复杂的古典音乐场景之上，其中时间对齐的乐谱和作品的多次演奏可用于训练、验证和测试算法。从技术角度来看，该项目研究了学习音级、多音高和显着性表示的方法。其中，将探索能够处理弱对齐注释的序列学习技术和受分层音乐结构启发的 U-net 架构。将学习到的表示应用于复杂的音乐场景，旨在通过利用新颖的深度学习算法的潜力来开发强大的音调分析方法，从而为计算音乐研究的新水平铺平道路。