基于语法的层次化表示改进符号化鼓风格分类
Improved symbolic drum style classification with grammar-based hierarchical representations
AI总结：
研究提出基于上下文无关音乐语法的MIDI树状层次表示，明确编码节奏信息，在GrooveMIDI Dataset上提升鼓风格分类性能且更紧凑高效。
摘要：
深度学习模型已成为分析和分类音乐数据的关键工具。这些模型要么在音频信号上运行，例如波形或频谱图，要么在符号表示上运行，例如MIDI。在后者中，音乐信息通常被简化为基本特征，即持续时间、音高和力度。大多数现有工作依赖于经典自然语言处理的通用分词策略，或矩阵表示，例如piano roll。在这项工作中，我们评估了符号数据的丰富表示如何影响深度模型，即Transformers和RNN，用于音乐风格分类。特别是，我们研究了明确包含MIDI类编码中隐含存在的音乐信息的表示，例如节奏组织，并表明它们优于通用分词策略。我们引入了一种基于上下文无关音乐语法的MIDI数据树状表示。我们表明，这种语法表示准确编码了高级节奏信息，并在GrooveMIDI Dataset上用于鼓风格分类时优于现有编码，同时更紧凑且参数效率更高。
英文摘要：
Deep learning models have become a critical tool for analysis and classification of musical data. These models operate either on the audio signal, e.g. waveform or spectrogram, or on a symbolic representation, such as MIDI. In the latter, musical information is often reduced to basic features, i.e. durations, pitches and velocities. Most existing works then rely on generic tokenization strategies from classical natural language processing, or matrix representations, e.g. piano roll. In this work, we evaluate how enriched representations of symbolic data can impact deep models, i.e. Transformers and RNN, for music style classification. In particular, we examine representations that explicitly incorporate musical information implicitly present in MIDI-like encodings, such as rhythmic organization, and show that they outperform generic tokenization strategies. We introduce a new tree-based representation of MIDI data built upon a context-free musical grammar. We show that this grammar representation accurately encodes high-level rhythmic information and outperforms existing encodings on the GrooveMIDI Dataset for drumming style classification, while being more compact and parameter-efficient.
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