JamSketch: Improvisation Support System with GA-Based Melody Creation from User’s Drawing

Kitahara, Tetsuro; Giraldo, Sergio; Ramírez, Rafael

doi:10.1007/978-3-030-01692-0_34

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…In music, a chord is an arbitrary harmonic set consisting of three or more notes, which sounds as if these notes are sounding simultaneously [18]. Conventionally, methods based on hidden Markov models (HMMs) [19][20][21] and genetic algorithms (GA) [22] are commonly used to deal with the task.…”

Section: Automatic Melody Harmonizationmentioning

confidence: 99%

SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours

Chen,

Lee,

Chen

et al. 2021

Preprint

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The surprisingness of a song is an essential and seemingly subjective factor in determining whether the listener likes it. With the help of information theory, it can be described as the transition probability of a music sequence modeled as a Markov chain. In this study, we introduce the concept of deriving entropy variations over time, so that the surprise contour of each chord sequence can be extracted. Based on this, we propose a user-controllable framework that uses a conditional variational autoencoder (CVAE) to harmonize the melody based on the given chord surprise indication. Through explicit conditions, the model can randomly generate various and harmonic chord progressions for a melody, and the Spearman's correlation and p-value significance show that the resulting chord progressions match the given surprise contour quite well. The vanilla CVAE model was evaluated in a basic melody harmonization task (no surprise control) in terms of six objective metrics. The results of experiments on the Hooktheory Lead Sheet Dataset show that our model achieves performance comparable to the state-of-the-art melody harmonization model.

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Section: Automatic Melody Harmonizationmentioning

confidence: 99%

SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours

Chen,

Lee,

Chen

et al. 2021

Preprint

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“…Here, we design a GA-based melody harmonization model by adapting the GAbased melody generation model proposed by (Kitahara et al, 2018). Unlike the other implemented models, the GA-based model takes as input a computed feature vector for every 16-th note (i.e., 1/4 beats).…”

Section: Genetic Algorithm (Ga)-based Modelmentioning

confidence: 99%

“…(1) We implement a set of melody harmonization models which span a number of canonical approaches to the task, including template matching, hidden Markov model (HMM) (Simon et al, 2008), genetic algorithm (GA) (Kitahara, Giraldo, & Ramirez, 2018), and two variants of deep recurrent neural network models (Lim et al, 2017). We then present a comparative study comparing the performance of these models.…”

Section: Introductionmentioning

confidence: 99%

Automatic Melody Harmonization with Triad Chords: A Comparative Study

Yeh

Hsiao

Fukayama

et al. 2020

Preprint

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Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task, including a template matching based model, a hidden Markov based model, a genetic algorithm based model, and two deep learning based models. The evaluation is conducted on a dataset of 9,226 melody/chord pairs we newly collect for this study, considering up to 48 triad chords, using a standardized training/test split. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants.

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“…Many approaches have been proposed for automatic melody harmonization [3], such as hidden Markov models (HMMs) [4,5,6] and genetic algorithm (GA)-based methods [7]. Recently, with the prevalence of deep learning models, some deep learning methods have emerged to deal with the melody harmonization problem [8].…”

Section: Introductionmentioning

confidence: 99%

Melody Harmonization Using Orderless Nade, Chord Balancing, and Blocked Gibbs Sampling

Sun

Chen

Lee

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Coherence and interestingness are two criteria for evaluating the performance of melody harmonization, which aims to generate a chord progression from a symbolic melody. In this study, we apply the concept of orderless NADE, which takes the melody and its partially masked chord sequence as the input of the BiLSTM-based networks to learn the masked ground truth, to the training process. In addition, the class weights are used to compensate for some reasonable chord labels that are rarely seen in the training set. Consistent with the stochasticity in training, blocked Gibbs sampling with proper numbers of masking/generating loops is used in the inference phase to progressively trade the coherence of the generated chord sequence off against its interestingness. The experiments were conducted on a dataset of 18,005 melody/chord pairs. Our proposed model outperforms the state-of-the-art system MTHarmonizer in five of six different objective metrics based on chord/melody harmonicity and chord progression. The subjective test results with more than 100 participants also show the superiority of our model.

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JamSketch: Improvisation Support System with GA-Based Melody Creation from User’s Drawing

Cited by 8 publications

References 6 publications

SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours

SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours

Automatic Melody Harmonization with Triad Chords: A Comparative Study

Melody Harmonization Using Orderless Nade, Chord Balancing, and Blocked Gibbs Sampling

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