Learning Hierarchical Metrical Structure Beyond Measures

• URL: http://arxiv.org/abs/2209.10259v1
• Date: Wed, 21 Sep 2022 11:08:52 GMT
• Title: Learning Hierarchical Metrical Structure Beyond Measures
• Authors: Junyan Jiang, Daniel Chin, Yixiao Zhang, Gus Xia
• Abstract summary: hierarchical structure annotations are helpful for music information retrieval and computer musicology. We propose a data-driven approach to automatically extract hierarchical metrical structures from scores. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings.
• Score: 3.7294116330265394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate training and evaluation. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings. We also perform some simple musicological analysis on the model predictions. All demos, datasets and pre-trained models are publicly available on Github.

Related papers

• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• Self-Supervised Hierarchical Metrical Structure Modeling [3.167685495996986]
  We propose a novel method to model hierarchical metrical structures for both symbolic music and audio signals. The model trains and inferences on beat-aligned music signals and predicts an 8-layer hierarchical metrical tree from beat, measure to the section level. All demos, source code and pre-trained models are publicly available on GitHub.
  arXiv  Detail & Related papers  (2022-10-31T10:05:19Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Exploring single-song autoencoding schemes for audio-based music structure analysis [6.037383467521294]
  This work explores a "piece-specific" autoencoding scheme, in which a low-dimensional autoencoder is trained to learn a latent/compressed representation specific to a given song. We report that the proposed unsupervised auto-encoding scheme achieves the level of performance of supervised state-of-the-art methods with 3 seconds tolerance.
  arXiv  Detail & Related papers  (2021-10-27T13:48:25Z)
• Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure [0.0]
  We propose a conditional melody generation model based on a hierarchical recurrent neural network. This model generates melodies with long-term structures based on given chord accompaniments. Results from our listening test indicate that CM-HRNN outperforms AttentionRNN in terms of long-term structure and overall rating.
  arXiv  Detail & Related papers  (2021-02-19T08:22:26Z)
• 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)
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music [69.2737664640826]
  This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
  arXiv  Detail & Related papers  (2020-12-02T14:19:19Z)
• Score-informed Networks for Music Performance Assessment [64.12728872707446]
  Deep neural network-based methods incorporating score information into MPA models have not yet been investigated. We introduce three different models capable of score-informed performance assessment.
  arXiv  Detail & Related papers  (2020-08-01T07:46:24Z)
• dMelodies: A Music Dataset for Disentanglement Learning [70.90415511736089]
  We present a new symbolic music dataset that will help researchers demonstrate the efficacy of their algorithms on diverse domains. This will also provide a means for evaluating algorithms specifically designed for music. The dataset is large enough (approx. 1.3 million data points) to train and test deep networks for disentanglement learning.
  arXiv  Detail & Related papers  (2020-07-29T19:20:07Z)
• Music Generation with Temporal Structure Augmentation [0.0]
  The proposed method augments a connectionist generation model with count-down to song conclusion and meter markers as extra input features. An RNN architecture with LSTM cells is trained on the Nottingham folk music dataset in a supervised sequence learning setup. Experiments show an improved prediction performance for both types of annotation.
  arXiv  Detail & Related papers  (2020-04-21T19:19:58Z)
• 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.