Chroma Toolbox: Matlab Implementations for Extracting Variants of Chroma-Based Audio Features

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 computing 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 features. Providing the MATLAB implementations on a welldocumented website under a GNU-GPL license, our aim is to foster research in music information retrieval. As another goal, we want to raise awareness that there is no single 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]  Meinard Müller,et al.  Audio Matching via Chroma-Based Statistical Features , 2005, ISMIR.

[2]  Hugo Fastl,et al.  Psychoacoustics: Facts and Models , 1990 .

[3]  Daniel P. W. Ellis,et al.  Identifying `Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Mark B. Sandler,et al.  Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations , 2005, ISMIR.

[5]  Gregory H. Wakefield,et al.  Audio thumbnailing of popular music using chroma-based representations , 2005, IEEE Transactions on Multimedia.

[6]  William M. Hartmann,et al.  Psychoacoustics: Facts and Models , 2001 .

[7]  Meinard Müller,et al.  Towards Timbre-Invariant Audio Features for Harmony-Based Music , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[9]  Takuya Fujishima,et al.  Realtime Chord Recognition of Musical Sound: a System Using Common Lisp Music , 1999, ICMC.

[10]  Meinard Müller,et al.  Audio-based Music Structure Analysis , 2010 .

[11]  Simon Dixon,et al.  Simultaneous Estimation of Chords and Musical Context From Audio , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  George Tzanetakis,et al.  Polyphonic audio matching and alignment for music retrieval , 2003, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684).

[13]  Xavier Serra,et al.  Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Gaël Richard,et al.  A comparative study of tonal acoustic features for a symbolic level music-to-score alignment , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Emilia Gómez Gutiérrez,et al.  Tonal description of music audio signals , 2006 .

[16]  R. Shepard.  Circularity in Judgments of Relative Pitch , 1964 .

[17]  Anssi Klapuri,et al.  Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Meinard Müller,et al.  Efficient Index-Based Audio Matching , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[19]  Juan Pablo Bello,et al.  A Robust Mid-Level Representation for Harmonic Content in Music Signals , 2005, ISMIR.