BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales

• URL: http://arxiv.org/abs/2109.07623v1
• Date: Wed, 15 Sep 2021 23:39:45 GMT
• Title: BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales
• Authors: Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin
• Abstract summary: BacHMMachine employs a "theory-driven" framework guided by music composition principles. It provides a probabilistic framework for learning key modulations and chordal progressions from a given melodic line. It results in vast decreases in computational burden and greater interpretability.
• Score: 23.64897650817862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists. One genre of particular interest is the four-part Baroque chorales of J.S. Bach. Methods for algorithmic chorale harmonization typically adopt a black-box, "data-driven" approach: they do not explicitly integrate principles from music theory but rely on a complex learning model trained with a large amount of chorale data. We propose instead a new harmonization model, called BacHMMachine, which employs a "theory-driven" framework guided by music composition principles, along with a "data-driven" model for learning compositional features within this framework. As its name suggests, BacHMMachine uses a novel Hidden Markov Model based on key and chord transitions, providing a probabilistic framework for learning key modulations and chordal progressions from a given melodic line. This allows for the generation of creative, yet musically coherent chorale harmonizations; integrating compositional principles allows for a much simpler model that results in vast decreases in computational burden and greater interpretability compared to state-of-the-art algorithmic harmonization methods, at no penalty to quality of harmonization or musicality. We demonstrate this improvement via comprehensive experiments and Turing tests comparing BacHMMachine to existing methods.

Related papers

• A Survey of Music Generation in the Context of Interaction [3.6522809408725223]
  Machine learning has been successfully used to compose and generate music, both melodies and polyphonic pieces. Most of these models are not suitable for human-machine co-creation through live interaction.
  arXiv  Detail & Related papers  (2024-02-23T12:41:44Z)
• Simple and Controllable Music Generation [94.61958781346176]
  MusicGen is a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns.
  arXiv  Detail & Related papers  (2023-06-08T15:31:05Z)
• Emotion-Conditioned Melody Harmonization with Hierarchical Variational Autoencoder [11.635877697635449]
  We propose a novel LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the influence of emotional conditions on melody harmonization. LHVAE incorporates latent variables and emotional conditions at different levels to model the global and local music properties. Objective experimental results show that our proposed model outperforms other LSTM-based models.
  arXiv  Detail & Related papers  (2023-06-06T14:28:57Z)
• A framework to compare music generative models using automatic evaluation metrics extended to rhythm [69.2737664640826]
  This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.
  arXiv  Detail & Related papers  (2021-01-19T15:04:46Z)
• Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models [70.70479436076238]
  We introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) The approach is underpinned by an inductive framework, which manipulates a candidate BC structured as a neural network, and a sound verifier, which either certifies the candidate's validity or generates counter-examples. The outcomes show that we can synthesise sound BCs up to two orders of magnitude faster, with in particular a stark speedup on the verification engine.
  arXiv  Detail & Related papers  (2020-07-07T07:39:42Z)
• Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach [74.09517278785519]
  We introduce a grading function that evaluates four-part chorales in the style of J.S. Bach along important musical features. We show that the function is both interpretable and outperforms human experts at discriminating Bach chorales from model-generated ones.
  arXiv  Detail & Related papers  (2020-06-23T21:02:55Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)
• RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning [69.20460466735852]
  This paper presents a deep reinforcement learning algorithm for online accompaniment generation. The proposed algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part.
  arXiv  Detail & Related papers  (2020-02-08T03:53:52Z)
• Continuous Melody Generation via Disentangled Short-Term Representations and Structural Conditions [14.786601824794369]
  We present a model for composing melodies given a user specified symbolic scenario combined with a previous music context. Our model is capable of generating long melodies by regarding 8-beat note sequences as basic units, and shares consistent rhythm pattern structure with another specific song. Results show that the music generated by our model tends to have salient repetition structures, rich motives, and stable rhythm patterns.
  arXiv  Detail & Related papers  (2020-02-05T06:23:44Z)
• Modeling Musical Structure with Artificial Neural Networks [0.0]
  I explore the application of artificial neural networks to different aspects of musical structure modeling. I show how a connectionist model, the Gated Autoencoder (GAE), can be employed to learn transformations between musical fragments. I propose a special predictive training of the GAE, which yields a representation of polyphonic music as a sequence of intervals.
  arXiv  Detail & Related papers  (2020-01-06T18:35:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.