Chord Detection Research Articles

Detecting chord tone alterations and suspensions

Chord detection is the task of assigning chord labels to segments of an input musical piece. The labels are typically drawn from a pre-defined vocabulary in which each symbol comprises at least a root pitch and a chord quality. In this paper, we introduce a post-processing method that takes as input a musical score with such labels and adds chord tone alterations such as suspensions to them. Although it is an important aspect of harmonic analysis, it is infeasible to do so by simply adding features to the labels in the vocabulary, given the multiplicative effect on vocabulary sizes which can already be over 1000. We evaluate our method using both ground truth input and input derived from a chord detection model, showing that it outperforms a strong heuristic baseline in both cases. Furthermore, we show that our model can be used to detect the rarer chord qualities from the model's output alterations, potentially enabling chord detection models to be trained on small vocabularies containing only the more common (and easier to identify) chord qualities (e.g. only triads).

The Annotated Mozart Sonatas: Score, Harmony, and Cadence

This article describes a new expert-labelled dataset featuring harmonic, phrase, and cadence analyses of all piano sonatas by W.A. Mozart. The dataset draws on the DCML standard for harmonic annotation and is being published adopting the FAIR principles of Open Science. The annotations have been verified using a data triangulation procedure which is presented as an alternative approach to handling annotator subjectivity. This procedure is suited for ensuring consistency, within the dataset and beyond, despite the high level of analytical detail afforded by the employed harmonic annotation syntax. The harmony labels also encode contextual information and are therefore suited for investigating music theoretical questions related to tonal harmony and the harmonic makeup of cadences in the classical style. Apart from providing basic statistical analyses characterizing the dataset, its music theoretical potential is illustrated by two preliminary experiments, one on the terminal harmonies of cadences and the other on the relation between performance durations and harmonic density. Furthermore, particular features can be selected to produce more coarse-grained training data, for example for chord detection algorithms that require less analytical detail. Facilitating the dataset’s reusability, it comes with a Python script that allows researchers to easily access various representations of the data tailored to their particular needs.

Symbolic Representation-based Melody Extraction using Multiclass Classification for Traditional Javanese Compositions

Traditional Javanese compositions contain melodies and skeletal melodies. Skeletal melodies are an extraction form of melodies. The melody extraction problem is similar to the chord detection in Western music, where chords are extracted from a melody. This research aims to develop a melody extraction system for traditional Javanese compositions. Melodies which have a time series data structure were designed as a part of the supervised learning problem to be solved using the pattern recognition technique and the Feed-Forward Neural Networks method. The melody data source uses a symbolic format in the form of sheet music. The beats in melodies data are used as the input and notes in skeletal melodies are used as the target. An FFNN multi-class classifier was built with six classes as the targets, where the class represents notes of the musical scale system. The network evaluation was conducted using accuracy, precision, recall, specificity and F-1 score measurements.

Music Theoretic and Perception-based Features for Audio Key Determination

The musical key of a piece is the fundamental knowledge for many Music Information Retrieval tasks as automatic transcription, chord detection or automatic play list generation. To this end, novel features are proposed and evaluated on the basis of musical knowledge, in this article—a total of 13 feature groups based on chromatic representation include scales, chords, major and minor Probe Tone Ratings, and further derived variations. We present extensive results on a database containing 35 h of originally recorded music covering diverse genres as popular, classical, and Jazz music. In the pre-processing, adjustment of reference pitch classes, dB(A)-weighting, band-pass-filter type selection, frequency selection, and gating time are discussed in detail. In addition to traditional correlation-based approaches we employ a data-driven model using Support Vector Machines. Overall, the proposed features enhance correlation results and their combination boosts data-driven audio key determination accuracy. The latter approach based on learnt models significantly outperforms correlation-based determination by 5.0% and 6.7% absolute for the determination of 12 and 24 major and minor keys (clustered or separately), respectively. Maximum accuracy reaches 77.3% and 62.1% for these 12 or 24 keys over the diverse recordings.

Simultaneous Estimation of Chords and Musical Context From Audio

Chord labels provide a concise description of musical harmony. In pop and jazz music, a sequence of chord labels is often the only written record of a song, and forms the basis of so-called lead sheets. We devise a fully automatic method to simultaneously estimate from an audio waveform the chord sequence including bass notes, the metric positions of chords, and the key. The core of the method is a six-layered dynamic Bayesian network, in which the four hidden source layers jointly model metric position, key, chord, and bass pitch class, while the two observed layers model low-level audio features corresponding to bass and treble tonal content. Using 109 different chords our method provides substantially more harmonic detail than previous approaches while maintaining a high level of accuracy. We show that with 71% correctly classified chords our method significantly exceeds the state of the art when tested against manually annotated ground truth transcriptions on the 176 audio tracks from the MIREX 2008 Chord Detection Task. We introduce a measure of segmentation quality and show that bass and meter modeling are especially beneficial for obtaining the correct level of granularity.

Chord detection method and apparatus for detecting a chord progression of an input melody

A chord detection method and apparatus for detecting a chord progression for accompaniment of an input melody on a basis of a desired chord applied to a portion of an series of tone pitch sections of the input melody, wherein harmonic tones are extracted from the respective tone pitch sections of the input melody to enumerate each constituent tone of the harmonic tones as a chord candidate for each of the tone pitch sections, and wherein the applied chord and the chord candidate are retrieved in the order of priority with reference to a chord progression suitable for the input melody to determine a chord coincident with the chord progression as each chord of the tone pitch sections.

Performance information analyzer and chord detection device associated therewith

A performance information analyzer wherein one of tone pitch information data applied thereto in response to progress of performance of a musical tune is analyzed into one of plural performance parts on a basis of a difference in tone pitch between the one of the tone pitch information data and a reference tone pitch information data previously assigned to a predetermined part of the plural performance parts during prior analysis of the tone pitch information and the other tone pitch information data are analyzed into the other performance parts on a basis of a difference in tone pitch between the analyzed tone pitch information data and each of the other tone pitch information data.