Jazz Contrafact Detection
- URL: http://arxiv.org/abs/2208.00792v1
- Date: Mon, 1 Aug 2022 12:07:20 GMT
- Title: Jazz Contrafact Detection
- Authors: C. Bunks and T. Weyde
- Abstract summary: In jazz, a contrafact is a new melody composed over an existing, but often reharmonized chord progression.
This paper develops a novel vector-space model to represent chord progressions, and uses it for contrafact detection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In jazz, a contrafact is a new melody composed over an existing, but often
reharmonized chord progression. Because reharmonization can introduce a wide
range of variations, detecting contrafacts is a challenging task. This paper
develops a novel vector-space model to represent chord progressions, and uses
it for contrafact detection. The process applies principles from music theory
to reduce the dimensionality of chord space, determine a common key signature
representation, and compute a chordal co-occurrence matrix. The rows of the
matrix form a basis for the vector space in which chord progressions are
represented as piecewise linear functions, and harmonic similarity is evaluated
by computing the membrane area, a novel distance metric. To illustrate our
method's effectiveness, we apply it to the Impro-Visor corpus of 2,612 chord
progressions, and present examples demonstrating its ability to account for
reharmonizations and find contrafacts.
Related papers
- An End-to-End Approach for Chord-Conditioned Song Generation [14.951089833579063]
Song Generation task aims to synthesize music composed of vocals and accompaniment from given lyrics.
To mitigate the issue, we introduce an important concept from music composition, namely chords to song generation networks.
We propose a novel model termed Chord-Conditioned Song Generator (CSG) based on it.
arXiv Detail & Related papers (2024-09-10T08:07:43Z) - Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation [16.790582113573453]
Emotion-driven melody aims to generate diverse harmonies for a single melody to convey desired emotions.
Previous research found it hard to alter the perceived emotional valence of lead sheets only by harmonizing the same melody with different chords.
In this paper, we propose a novel functional representation for symbolic music.
arXiv Detail & Related papers (2024-07-29T17:05:12Z) - REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning [64.08293076551601]
We propose a novel method of using a learned measure for identifying positive pairs.
Our Retrieval-Based Reconstruction measure measures the similarity between two sequences.
We show that the REBAR error is a predictor of mutual class membership.
arXiv Detail & Related papers (2023-11-01T13:44:45Z) - Melody transcription via generative pre-training [86.08508957229348]
Key challenge in melody transcription is building methods which can handle broad audio containing any number of instrument ensembles and musical styles.
To confront this challenge, we leverage representations from Jukebox (Dhariwal et al. 2020), a generative model of broad music audio.
We derive a new dataset containing $50$ hours of melody transcriptions from crowdsourced annotations of broad music.
arXiv Detail & Related papers (2022-12-04T18:09:23Z) - And what if two musical versions don't share melody, harmony, rhythm, or
lyrics ? [2.4366811507669124]
We show that an approximated representation of the lyrics is an efficient proxy to discriminate between versions and non-versions.
We then describe how these features complement each other and yield new state-of-the-art performances on two publicly available datasets.
arXiv Detail & Related papers (2022-10-03T22:33:14Z) - Contrastive Learning with Positive-Negative Frame Mask for Music
Representation [91.44187939465948]
This paper proposes a novel Positive-nEgative frame mask for Music Representation based on the contrastive learning framework, abbreviated as PEMR.
We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.
arXiv Detail & Related papers (2022-03-17T07:11:42Z) - Chord-Conditioned Melody Choralization with Controllable Harmonicity and
Polyphonicity [75.02344976811062]
Melody choralization, i.e. generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. Bach chorales.
Previous neural network-based systems rarely focus on chorale generation conditioned on a chord progression.
We propose DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression.
arXiv Detail & Related papers (2022-02-17T02:59:36Z) - A-Muze-Net: Music Generation by Composing the Harmony based on the
Generated Melody [91.22679787578438]
We present a method for the generation of Midi files of piano music.
The method models the right and left hands using two networks, where the left hand is conditioned on the right hand.
The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony.
arXiv Detail & Related papers (2021-11-25T09:45:53Z) - BacHMMachine: An Interpretable and Scalable Model for Algorithmic
Harmonization for Four-part Baroque Chorales [23.64897650817862]
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.
arXiv Detail & Related papers (2021-09-15T23:39:45Z) - Generating Lead Sheets with Affect: A Novel Conditional seq2seq
Framework [3.029434408969759]
We present a novel approach for calculating the positivity or negativity of a chord progression within a lead sheet.
Our approach is similar to a Neural Machine Translation (NMT) problem, as we include high-level conditions in the encoder part of the sequence-to-sequence architectures.
The proposed strategy is able to generate lead sheets in a controllable manner, resulting in distributions of musical attributes similar to those of the training dataset.
arXiv Detail & Related papers (2021-04-27T09:04:21Z) - WaveTransform: Crafting Adversarial Examples via Input Decomposition [69.01794414018603]
We introduce WaveTransform', that creates adversarial noise corresponding to low-frequency and high-frequency subbands, separately (or in combination)
Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.
arXiv Detail & Related papers (2020-10-29T17:16:59Z)
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.