Copyright: (c) 2014 Christof Weiß et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Dixon, S

Mauch, M

Weiss, C

English

Christof Weiß

Matthias Mauch

Simon Dixon

University of Michigan Library Repository

Timbre-invariant Audio Features for Style Analysis of Classical Music

We propose a novel set of chroma-based audio features inspired by pitch class set theory and show their utility for style analysis of classical music by using them to classify recordings into historical periods. Musicologists have long studied how composers’ styles develop and influence each other, but usually based on manual analyses of the score or, more recently, automatic analyses on symbolic data, both largely independent from timbre. Here, we investigate whether such musical style analyses can be realized using audio features. Based on chroma, our features describe the use of intervals and triads on multiple time scales. To test the efficacy of this approach we use a 1600 track balanced corpus that covers the Baroque, Classical, Romantic and Modern eras, and calculate features based on four different chroma extractors and several parameter configurations. Using Linear Discriminant Analysis, our features allow for a visual separation of the four eras that is invariant to timbre. Classification using Support Vector Machines shows that a high era classification accuracy can be achieved despite strong timbral variation (piano vs. orchestra) within eras. Under the optimal parameter configuration, the classifier achieves accuracies of 82.5%

Weiss, Christof

Mauch, Matthias

Dixon, Simon

Fraunhofer-ePrints

Timbre-invariant audio features for style analysis of classical music

Queen Mary Research Online

A machine learning approach to musical style recognition,”

A quantitative study of chromaticism: Changes observed in historical eras and individual composers,”

Analyzing chroma feature types for automated chord recognition,”

Approximate note transcription for the improved identification of difficult chords,”

Audio matching via chroma-based statistical features,”

Automatic chord recognition from audio using enhanced pitch class profile,”

Automatic classification of musical pieces into global cultural areas,”

Automatic genre classification using large high-level musical feature sets,”

Automatic subgenre classification of heavy metal music,” Master’s thesis,

Characterisation of composer style using high level musical features,”

Chroma toolbox: Matlab implementations for extracting variants of chroma-based audio features,”

Chromabased statistical audio features for audio matching,”

Clustering and classification of music by interval categories,”

Composer attribution by quantifying compositional strategies,”

Detection of historical period in symbolic music text,”

Evaluation and comparison of audio chroma feature extraction methods,”

Historical or stylistic periods?”

Information Retrieval for Music and Motion, 1st ed. Berlin and Heidelberg:

LIBSVM: a library for support vector machines,”

Musical style recognition - a quantitative approach,”

N-gram chord profiles for composer style identification,”

Pattern recognition approach for music style identification using shallow statistical descriptors,”

Peachnote: music score search and analusis platform,”

Perceptual basis of evolving western musical styles,”

Pitch class set categories as analysis tools for degrees of tonality,”

Statistical Pattern Recognition, 2nd ed.

Style periods of music history considered analytically,”

The eighteenth century as a music-historical period?”

To catch a chorus: Using chroma-based representations for audio thumbnailing,”

Tonal description of music audio signals,”

Christof Weiss

NEUROSURGERY ENTHUSIASTIC WOMEN SOCIETY

ZENODO

http://hdl.handle.net/2027/spo.bbp2372.2014.222

Timbre-invariant Audio Features for Style Analysis of Classical Music

Abstract

Similar works

Full text

Available Versions

University of Michigan Library Repository

Fraunhofer-ePrints

Queen Mary Research Online

NEUROSURGERY ENTHUSIASTIC WOMEN SOCIETY

ZENODO