CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions

• URL: http://arxiv.org/abs/2410.22046v1
• Date: Tue, 29 Oct 2024 13:53:09 GMT
• Title: CHORDONOMICON: A Dataset of 666,000 Songs and their Chord Progressions
• Authors: Spyridon Kantarelis, Konstantinos Thomas, Vassilis Lyberatos, Edmund Dervakos, Giorgos Stamou,
• Abstract summary: Chordonomicon is a dataset of over 666,000 songs and their chord progressions, annotated with structural parts, genre, and release date. These characteristics make the Chordonomicon an ideal testbed for exploring advanced machine learning techniques.
• Score: 1.8541450825478398
• License:
• Abstract: Chord progressions encapsulate important information about music, pertaining to its structure and conveyed emotions. They serve as the backbone of musical composition, and in many cases, they are the sole information required for a musician to play along and follow the music. Despite their importance, chord progressions as a data domain remain underexplored. There is a lack of large-scale datasets suitable for deep learning applications, and limited research exploring chord progressions as an input modality. In this work, we present Chordonomicon, a dataset of over 666,000 songs and their chord progressions, annotated with structural parts, genre, and release date - created by scraping various sources of user-generated progressions and associated metadata. We demonstrate the practical utility of the Chordonomicon dataset for classification and generation tasks, and discuss its potential to provide valuable insights to the research community. Chord progressions are unique in their ability to be represented in multiple formats (e.g. text, graph) and the wealth of information chords convey in given contexts, such as their harmonic function . These characteristics make the Chordonomicon an ideal testbed for exploring advanced machine learning techniques, including transformers, graph machine learning, and hybrid systems that combine knowledge representation and machine learning.

Related papers

• Foundation Models for Music: A Survey [77.77088584651268]
  Foundations models (FMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music.
  arXiv  Detail & Related papers  (2024-08-26T15:13:14Z)
• A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis [20.718872433254806]
  Schenkerian Analysis (SchA) is a method of music analysis that combines elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music. SchA has rarely been utilized by the computer music community. With a larger corpus of Schenkerian data, it may be possible to infuse machine learning models with a deeper understanding of musical structure, thus leading to more "human" results.
  arXiv  Detail & Related papers  (2024-08-13T19:52:06Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Graph-based Polyphonic Multitrack Music Generation [9.701208207491879]
  This paper introduces a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times.
  arXiv  Detail & Related papers  (2023-07-27T15:18:50Z)
• 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)
• A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
  This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
  arXiv  Detail & Related papers  (2022-11-21T14:15:43Z)
• MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification [1.8275108630751844]
  Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field. Most of the existing models are designed for class-balanced music datasets. We propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes.
  arXiv  Detail & Related papers  (2022-09-09T03:52:44Z)
• Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation [158.54649047794794]
  Re-creation of Creations (ROC) is a new paradigm for lyric-to-melody generation. ROC achieves good lyric-melody feature alignment in lyric-to-melody generation.
  arXiv  Detail & Related papers  (2022-08-11T08:44:47Z)
• Multi-task Learning with Metadata for Music Mood Classification [0.0]
  Mood recognition is an important problem in music informatics and has key applications in music discovery and recommendation. We propose a multi-task learning approach in which a shared model is simultaneously trained for mood and metadata prediction tasks. Applying our technique on the existing state-of-the-art convolutional neural networks for mood classification improves their performances consistently.
  arXiv  Detail & Related papers  (2021-10-10T11:36:34Z)
• 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)

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.