Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation

• URL: http://arxiv.org/abs/2407.20955v1
• Date: Tue, 30 Jul 2024 16:29:28 GMT
• Title: Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation
• Authors: Jingyue Huang, Ke Chen, Yi-Hsuan Yang,
• Abstract summary: Managing the emotional aspect remains a challenge in automatic music generation. This paper explores the disentanglement of emotions in piano performance generation through a two-stage framework.
• Score: 19.139752434303688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Managing the emotional aspect remains a challenge in automatic music generation. Prior works aim to learn various emotions at once, leading to inadequate modeling. This paper explores the disentanglement of emotions in piano performance generation through a two-stage framework. The first stage focuses on valence modeling of lead sheet, and the second stage addresses arousal modeling by introducing performance-level attributes. To further capture features that shape valence, an aspect less explored by previous approaches, we introduce a novel functional representation of symbolic music. This representation aims to capture the emotional impact of major-minor tonality, as well as the interactions among notes, chords, and key signatures. Objective and subjective experiments validate the effectiveness of our framework in both emotional valence and arousal modeling. We further leverage our framework in a novel application of emotional controls, showing a broad potential in emotion-driven music generation.

Related papers

• Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings [10.302353984541497]
  This research develops a model capable of generating music that resonates with the emotions depicted in visual arts. Addressing the scarcity of aligned art and music data, we curated the Emotion Painting Music dataset. Our dual-stage framework converts images to text descriptions of emotional content and then transforms these descriptions into music, facilitating efficient learning with minimal data.
  arXiv  Detail & Related papers  (2024-09-12T08:19:25Z)
• 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)
• Emotion Manipulation Through Music -- A Deep Learning Interactive Visual Approach [0.0]
  We introduce a novel way to manipulate the emotional content of a song using AI tools. Our goal is to achieve the desired emotion while leaving the original melody as intact as possible. This research may contribute to on-demand custom music generation, the automated remixing of existing work, and music playlists tuned for emotional progression.
  arXiv  Detail & Related papers  (2024-06-12T20:12:29Z)
• Are Words Enough? On the semantic conditioning of affective music generation [1.534667887016089]
  This scoping review aims to analyze and discuss the possibilities of music generation conditioned by emotions. In detail, we review two main paradigms adopted in automatic music generation: rules-based and machine-learning models. We conclude that overcoming the limitation and ambiguity of language to express emotions through music has the potential to impact the creative industries.
  arXiv  Detail & Related papers  (2023-11-07T00:19:09Z)
• A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition [76.65908232134203]
  Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks.
  arXiv  Detail & Related papers  (2022-01-15T07:45:10Z)
• Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning [0.0]
  The role of musical prosody remains under-explored despite several studies demonstrating a strong connection between prosody and emotion. In this study, we restrict the input of traditional machine learning algorithms to the features of musical prosody. We utilize a methodology for individual data collection from vocalists, and personal ground truth labeling by the artist themselves.
  arXiv  Detail & Related papers  (2021-06-04T15:40:19Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Audio-Driven Emotional Video Portraits [79.95687903497354]
  We present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits.
  arXiv  Detail & Related papers  (2021-04-15T13:37:13Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)
• Facial Expression Editing with Continuous Emotion Labels [76.36392210528105]
  Deep generative models have achieved impressive results in the field of automated facial expression editing. We propose a model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels.
  arXiv  Detail & Related papers  (2020-06-22T13:03:02Z)
• Music Gesture for Visual Sound Separation [121.36275456396075]
  "Music Gesture" is a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music. We first adopt a context-aware graph network to integrate visual semantic context with body dynamics, and then apply an audio-visual fusion model to associate body movements with the corresponding audio signals.
  arXiv  Detail & Related papers  (2020-04-20T17:53:46Z)

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.