MATLAB Chroma Toolbox: Variants & Audio Feature Extraction for I...
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本文档标题为《CHROMA TOOLBOX：MATLAB 实现的音频特征提取工具箱》（CHROMATOOLBOX: MATLAB IMPLEMENTATIONS FOR EXTRACTING VARIANTS OF CHROMA-BASED AUDIO FEATURES），发表在第12届国际音乐信息检索会议（ISMIR 2011）。作者Meinard Müller和Sebastian Ewert分别来自萨尔兰德大学和波恩大学计算机科学III系。
Chroma-based audio features，与和声密切相关，是音乐数据分析中的常用工具。这类特征计算和增强方法众多，导致产生了多种具有不同特性的Chroma变体。论文介绍了一个名为CHROMATOOLBOX的MATLAB工具箱，该工具箱集成了近期提出的基于音高和Chroma的音频特征的多种实现。其目标是促进音乐信息检索领域的研究，通过提供详尽文档的GitHub网站上的免费MATLAB代码，遵循GNU-GPL许可协议，鼓励研究人员在此基础上进行创新和改进。
论文强调了多样性的重要性，指出没有一种单一的Chroma变体适用于所有应用场景。作者通过两个具体的应用实例，讨论了选择合适的Chroma特征变体对音乐分析任务的影响。这表明在实际应用中，开发者需要根据特定的分析需求，灵活选择和定制适合的Chroma特征。因此，CHROMATOOLBOX不仅仅是一套代码库，更是一个平台，促进了Chroma特征研究的实用性和多样性的发展。
通过使用这个工具箱，研究者可以方便地实验不同的Chroma计算方法，比如用于音乐分类、情感识别或者音乐相似度计算等任务。同时，它也鼓励了跨学科的合作，使得音乐信息处理领域能够更好地利用和理解Chroma这一核心音频特征在现代音乐分析中的作用。CHROMATOOLBOX为音频特征工程和音乐信息检索技术的研究者提供了宝贵的资源和一个协作的起点。
12th International Society for Music Information Retrieval Conference (ISMIR 2011)
CHROMA TOOLBOX: MATLAB IMPLEMENTATIONS FOR EXTRACTING
VARIANTS OF CHROMA-BASED AUDIO FEATURES
Meinard M
¨
uller
Saarland University
and MPI Informatik
meinard@mpi-inf.mpg.de
Sebastian Ewert
Computer Science III
University of Bonn
ewerts@iai.uni-bonn.de
ABSTRACT
Chroma-based audio features, which closely correlate to the
aspect of harmony, are a well-established tool in processing
and analyzing music data. There are many ways of comput-
ing and enhancing chroma features, which results in a large
number of chroma variants with different properties. In this
paper, we present a chroma toolbox [13], which contains
MATLAB implementations for extracting various types of
recently proposed pitch-based and chroma-based audio fea-
tures. Providing the MATLAB implementations on a well-
documented website under a GNU-GPL license, our aim is
to foster research in music information retrieval. As an-
other goal, we want to raise awareness that there is no sin-
gle chroma variant that works best in all applications. To
this end, we discuss two example applications showing that
the final music analysis result may crucially depend on the
initial feature design step.
1. INTRODUCTION
It is a well-known phenomenon that human perception of
pitch is periodic in the sense that two pitches are perceived
as similar in “color” if they differ by an octave. Based on
this observation, a pitch can be separated into two com-
ponents, which are referred to as tone height and chroma,
see [19]. Assuming the equal-tempered scale, the chromas
correspond to the set {C, C
♯
, D, . . . , B} that consists of the
twelve pitch spelling attributes
1
as used in Western music
notation. Thus, a chroma feature is represented by a 12-
dimensional vector x = (x(1), x(2), . . . , x(12))
T
, where
x(1) corresponds to chroma C, x(2) to chroma C
♯
, and so
1
Note that in the equal-tempered scale different pitch spellings such C
♯
and D
♭
refer to the same chroma.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page.
c
2011 International Society for Music Information Retrieval.
CP
CLP
CENS
CRP
Audio
representation
Pitch
representation
Chroma
representation
Tuning
estimation
Multirate
pitch
filterbank
Smoothing
Logarithmic
compression
Quantization
Reduction
Normalization
Figure 1. Overview of the feature extraction pipeline.
on. In the feature extraction step, a given audio signal is
converted into a sequence of chroma features each express-
ing how the short-time energy of the signal is spread over
the twelve chroma bands.
Identifying pitches that differ by an octave, chroma fea-
tures show a high degree of robustness to variations in
timbre and closely correlate to the musical aspect of har-
mony. This is the reason why chroma-based audio fea-
tures, sometimes also referred to as pitch class profiles, are
a well-established tool for processing and analyzing music
data [1, 5, 12]. For example, basically every chord recog-
nition procedure relies on some kind of chroma represen-
tation [2, 4, 11]. Also, chroma features have become the
de facto standard for tasks such as music synchronization
and alignment [7, 8, 12], as well as audio structure analy-
sis [16]. Finally, chroma features have turned out to be a
powerful mid-level feature representation in content-based
audio retrieval such as cover song identification [3, 18] or
audio matching [10, 15].
There are many ways for computing chroma-based audio
features. For example, the conversion of an audio record-
ing into a chroma representation (or chromagram) may be
performed either by using short-time Fourier transforms in
combination with binning strategies [1] or by employing
suitable multirate filter banks [12]. Furthermore, the prop-
erties of chroma features can be significantly changed by
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