Liszt’s Étude S.136 no.1: audio data analysis of two different piano recordings

IF 1.4 4区计算机科学 Q2 STATISTICS & PROBABILITY Advances in Data Analysis and Classification Pub Date : 2024-04-26 DOI:10.1007/s11634-024-00594-6

Matteo Farnè

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Abstract

In this paper, we review the main signal processing tools of Music Information Retrieval (MIR) from audio data, and we apply them to two recordings (by Leslie Howard and Thomas Rajna) of Franz Liszt’s Étude S.136 no.1, with the aim of uncovering the macro-formal structure and comparing the interpretative styles of the two performers. In particular, after a thorough spectrogram analysis, we perform a segmentation based on the degree of novelty, in the sense of spectral dissimilarity, calculated frame-by-frame via the cosine distance. We then compare the metrical, temporal and timbrical features of the two executions by MIR tools. Via this method, we are able to identify in a data-driven way the different moments of the piece according to their melodic and harmonic content, and to find out that Rajna’s execution is faster and less various, in terms of intensity and timbre, than Howard’s one. This enquiry represents a case study able to show the potentialities of MIR from audio data in supporting traditional music score analyses and in providing objective information for statistically founded musical execution analyses.

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李斯特的 Étude S.136 no.1：两种不同钢琴录音的音频数据分析

本文回顾了从音频数据中进行音乐信息检索（MIR）的主要信号处理工具，并将其应用于弗朗兹-李斯特《Étude S.136 no.1》的两段录音（分别由莱斯利-霍华德和托马斯-拉吉纳录制），旨在揭示其宏观形式结构并比较两位演奏者的演绎风格。具体而言，在对频谱图进行全面分析后，我们根据新颖程度进行分段，即通过余弦距离逐帧计算出的频谱异同度。然后，我们通过 MIR 工具比较两次执行的韵律、时间和时态特征。通过这种方法，我们能够根据旋律和和声的内容，以数据驱动的方式识别乐曲的不同时刻，并发现拉吉纳的演奏比霍华德的演奏速度更快，在力度和音色方面的变化也更少。这项研究是一项案例研究，它展示了从音频数据中提取的 MIR 在支持传统乐谱分析以及为基于统计的音乐执行分析提供客观信息方面的潜力。

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来源期刊

Advances in Data Analysis and Classification STATISTICS & PROBABILITY-

CiteScore

3.40

自引率

6.20%

发文量

审稿时长

>12 weeks

期刊介绍： The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.