BREVE: An HMPerceptron-Based Chord Recognition System

Tonal harmony analysis is a sophisticated task. It combines general knowledge with contextual cues, and it is concerned with faceted and evolving objects such as musical language, execution style and taste. We present Breve, a system for performing a particular kind of harmony analysis, chord recognition: music is encoded as a sequence of sounding events and the system should assing the appropriate chord label to each event. The solution proposed to the problem relies on a conditional model, where domain knowledge is encoded in the form of Boolean features. Breve exploits the recently proposed algorithm CarpeDiem to obtain significant computational gains in solving the optimization problem underlying the classification process. The implemented system has been validated on a corpus of chorales from J.S. Bach: we report and discuss the learnt weights, point out the committed errors, and elaborate on the correlation between errors and growth in the classification times in places where the music is less clearly asserted.

[1]  Atsushi Imiya,et al.  Structural, Syntactic, and Statistical Pattern Recognition , 2012, Lecture Notes in Computer Science.

[2]  Edwin R. Hancock,et al.  Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings , 2010, SSPR/SPR.

[3]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[4]  William P. Birmingham,et al.  Automated Partitioning of Tonal Music , 2000, FLAIRS.

[5]  Maarten Grachten,et al.  A case based approach to expressivity-aware tempo transformation , 2006, Machine Learning.

[6]  Thomas G. Dietterich Machine Learning for Sequential Data: A Review , 2002, SSPR/SPR.

[7]  Jason Freeman Fast generation of audio signatures to describe iTunes libraries , 2006 .

[8]  Terry Winograd,et al.  Linguistics and the computer analysis of tonal harmony , 1968 .

[9]  Geber Ramalho,et al.  COCHONUT: Recognizing Complex Chords from MIDI Guitar Sequences , 2008, ISMIR.

[10]  David Cope,et al.  A Musical Learning Algorithm , 2004, Computer Music Journal.

[11]  Daniele P. Radicioni,et al.  CarpeDiem: an algorithm for the fast evaluation of SSL classifiers , 2007, ICML '07.

[12]  Roberto Basili,et al.  AI*IA 2007: Artificial Intelligence and Human-Oriented Computing, 10th Congress of the Italian Association for Artificial Intelligence, Rome, Italy, September 10-13, 2007, Proceedings , 2007, AI*IA.

[13]  Gerhard Widmer Guest Editorial: Machine learning in and for music , 2006, Machine Learning.

[14]  R. Jackendoff,et al.  A Generative Theory of Tonal Music , 1985 .

[15]  Gerhard Widmer,et al.  Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries , 2003, Artif. Intell..

[16]  Christopher Raphael,et al.  Functional Harmonic Analysis Using Probabilistic Models , 2004, Computer Music Journal.

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

[18]  Ian Bent Music Analysis in the Nineteenth Century: Elucidatory analysis , 1994 .

[19]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[20]  A. Schoenberg,et al.  Structural functions of harmony , 1954 .

[21]  Structural, Syntactic, and Statistical Pattern Recognition , 2002, Lecture Notes in Computer Science.

[22]  Thomas G. Dietterich,et al.  Training conditional random fields via gradient tree boosting , 2004, ICML.

[23]  Ramón López de Mántaras,et al.  Ai and Music: From Composition to Expressive Performance , 2002, AI Mag..

[24]  Daniele P. Radicioni,et al.  Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets , 2007, AI*IA.

[25]  Christopher Raphael,et al.  A hybrid graphical model for rhythmic parsing , 2002, Artif. Intell..

[26]  D. Temperley The Cognition of Basic Musical Structures , 2001 .

[27]  Andrew McCallum,et al.  Maximum Entropy Markov Models for Information Extraction and Segmentation , 2000, ICML.

[28]  Jérôme Barthélemy,et al.  Figured Bass and Tonality Recognition , 2001, ISMIR.

[29]  L. Demany,et al.  Harmonic and melodic octave templates. , 1990, The Journal of the Acoustical Society of America.

[30]  Pat Langley,et al.  Editorial: On Machine Learning , 1986, Machine Learning.

[31]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[32]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[33]  E. Bigand,et al.  Global context effects on musical expectancy , 1997, Perception & psychophysics.

[34]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[35]  William P. Birmingham,et al.  Algorithms for Chordal Analysis , 2002, Computer Music Journal.

[36]  Malcolm Slaney,et al.  Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio , 2008, IEEE Transactions on Audio, Speech, and Language Processing.