Learning Tonal Representations of Music Signals Using Deep Neural Networks

随着技术的影响日益扩大，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使大型音乐语料库的系统分析成为可能。最近，这样的语料库研究是基于音频记录进行的，涉及数字信号处理和机器学习的方法。在这种情况下，关于和弦、音阶或键的音乐信号的音调分析起着重要的作用。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，其指示随着时间的推移音乐音高类的存在，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是可解释的，鲁棒的和不变的。该项目建立在古典音乐的复杂场景上，其中时间对齐的乐谱和作品的多次表演可用于训练，验证和测试算法。从技术的角度来看，这个项目研究学习音高类，多音高，和显着性表示的方法。除此之外，还将探索可以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型网络架构。将学习到的表示应用于复杂的音乐场景，旨在通过利用新型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。

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.