Probabilistic Melodic Harmonization

Paiement, Jean-François; Eck, Douglas; Bengio, Samy

doi:10.1007/11766247_19

Cited by 24 publications

(13 citation statements)

References 6 publications

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“…Note that our algorithm is capable of predicting chords in real time as well; however, this is a fundamentally different problem than the one we address, as: (a) future information about a melody is unavailable in real time, and (b) predicted chords may interfere with a vocalist's melodic intentions. Paiement et al [13] use a multilevel graphical model to generate chord progressions to accompany a given melody. Though their model allows for longer-term dependencies than an HMM, it relies on songs being precisely 16 measures long.…”

Section: Automatic Harmonization and Chord Generationmentioning

confidence: 99%

MySong

Simon

Morris

Basu

2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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We introduce MySong, a system that automatically chooses chords to accompany a vocal melody. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone, and can experiment with different styles and chord patterns using interactions designed to be intuitive to non-musicians.We describe the implementation of MySong, which trains a Hidden Markov Model using a music database and uses that model to select chords for new melodies. Model parameters are intuitively exposed to the user. We present results from a study demonstrating that chords assigned to melodies using MySong and chords assigned manually by musicians receive similar subjective ratings. We then present results from a second study showing that thirteen users with no background in music theory are able to rapidly create musical accompaniments using MySong, and that these accompaniments are rated positively by evaluators.

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Section: Automatic Harmonization and Chord Generationmentioning

confidence: 99%

MySong

Simon

Morris

Basu

2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…A melody harmonization task requires capturing the longterm dependencies in music since a constrained sets of chord progressions can consistently interact with a given melody [4]. This has motivated the use of linguistic techniques such as context-free grammar [5], genetic algorithms [6], or hidden Markov models (HMMs) [3], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Translating Melody to Chord: Structured and Flexible Harmonization of Melody With Transformer

et al. 2022

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Recent deep learning approaches for melody harmonization have achieved remarkable performance by overcoming the uneven chord distributions of music data. However, most of these approaches have not attempted to capture an original melodic structure and generate structured chord sequences with appropriate rhythms. Hence, we use a Transformer-based architecture that directly maps lower-level melody notes into a semantic higher-level chord sequence. In particular, we encode the binary piano roll of a melody into a note-based representation. Furthermore, we address the flexible generation of various chords with Transformer expanded with a VAE framework. We propose three Transformer-based melody harmonization models: 1) the standard Transformer-based model for the neural translation of a melody to chords (STHarm), 2) the variational Transformer model for learning the global representation of complete music (VTHarm), and 3) the regularized variational Transformer model for the controllable generation of chords (rVTHarm). Experimental results demonstrate that the proposed models generate more structured, diverse chord sequences than LSTM-based models.

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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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Probabilistic Melodic Harmonization

Cited by 24 publications

References 6 publications

MySong

MySong

Translating Melody to Chord: Structured and Flexible Harmonization of Melody With Transformer

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

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