ChordRipple

Huang, Cheng-Zhi Anna; Duvenaud, David; Gajos, Krzysztof Z.

doi:10.1145/2856767.2856792

Cited by 36 publications

(2 citation statements)

References 9 publications

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“…The year 2016 is perhaps the starting point of neural network approaches to model music information, in particular, chord progressions using symbolic music notation. ChordRipple [7] presented a Chord2Vec encoder by adapting Word2Vec using datasets derived from the Bach chorales and The Rolling Stone Top 200 songs corpus. The chord vocabulary includes triads, tetrads, and inversions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies

Dalmazzo,

Déguernel,

Sturm

2024

Lecture Notes in Computer Science

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This paper compares two tokenization strategies for modeling chord progressions using the encoder transformer architecture trained with a large dataset of chord progressions in a variety of styles. The first strategy includes a tokenization method treating all different chords as unique elements, which results in a vocabulary of 5202 independent tokens. The second strategy expresses the chords as a dynamic tuple describing root, nature (e.g., major, minor, diminished, etc.), and extensions (e.g., additions or alterations), producing a specific vocabulary of 59 tokens related to chords and 75 tokens for style, bars, form, and format. In the second approach, MIDI embeddings are added into the positional embedding layer of the transformer architecture, with an array of eight values related to the notes forming the chords. We propose a trigram analysis addition to the dataset to compare the generated chord progressions with the training dataset, which reveals common progressions and the extent to which a sequence is duplicated. We analyze progressions generated by the models comparing HITS@k metrics and human evaluation of 10 participants, rating the plausibility of the progressions as potential music compositions from a musical perspective. The second model reported lower validation loss, better metrics, and more musical consistency in the suggested progressions.

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Section: Literature Reviewmentioning

confidence: 99%

The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies

Dalmazzo,

Déguernel,

Sturm

2024

Lecture Notes in Computer Science

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“…Major-minor tonality has long been present in Western Classical Music, providing means for shaping the mood of musical compositions. Nowadays, it plays an important role in many music-related algorithms or systems, for example music genre recognition [1][2][3], assessment of musical tension [4][5][6][7], music visualization systems [8][9][10][11], music data mining [6], computer-aided composition software [12][13][14], and determining the harmonic structure of created pieces [15,16]. It is worth mentioning that in recent years, methods implementing neural networks [17][18][19] and other machine learning approaches [20][21][22] have gained popularity in many areas of music analysis.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Music Key Detection Approaches Utilizing Key-Profiles with a New Method Based on the Signature of Fifths

et al. 2022

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This paper compares approaches to music key detection based on popular key-profiles with a new key detection method that utilizes the concept of the signature of fifths. The signature of fifths is a geometrical music harmonic-content descriptor. Depending on the scenario, it may reflect either the multiplicities of occurrences or the aggregated durations of individual pitch-classes of the chromatic scale in a given fragment of music. In this study, we compare the efficacies of a few strictly correlational key recognition approaches based on music key-profiles (i.e., Krumhansl–Kessler, Temperley, and Albrecht–Shanahan) with a new method that implements the concept of the signature of fifths. All the experiments were performed in a collection of music pieces comprised of preludes by J. S. Bach (The Well-Tempered Clavier-Book I), preludes by F. Chopin (Op. 28), and songs by The Beatles (from the album A Hard Day’s Night). In the scenario implementing the aggregate durations of individual pitch-classes, based on the analysis of the shortest initial fragments of music for which the key was indicated in all the considered approaches, the key detection efficacy obtained with the method using the signature of fifths was greater than the efficacies obtained with the strictly correlational approaches utilizing key-profiles (on average by 9.27 pp). In the case of the analogous analysis carried out for the scenario implementing the multiplicities of occurrences of individual pitch-classes, on average, greater efficacy was observed for the strictly correlational approaches based on key-profiles (by 2.7 pp). The conducted experiments confirmed the new key-detecting method offers advantages in computational simplicity, stability of decision making, and the ability to successfully determine the key based on a very short fragment of music.

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Automatic Estimation of Harmonic Tension by Distributed Representation of Chords

Nikrang

Sears

Widmer

2018

Music Technology With Swing

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The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications [27]. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of welldefined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model's predictions conform very well with empirical evidence obtained from human listeners.

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ChordRipple

Cited by 36 publications

References 9 publications

The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies

The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies

A Comparison of the Music Key Detection Approaches Utilizing Key-Profiles with a New Method Based on the Signature of Fifths

Automatic Estimation of Harmonic Tension by Distributed Representation of Chords

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