Related papers: Interactive Melody Generation System for Enhancing…

In this work, we introduce Mozualization, a music generation and editing tool that creates multi-style embedded music by integrating diverse inputs, such as keywords, images, and sound clips (e.g., segments from various pieces of music or…

Making music with other people is a social activity as well as an artistic one. Music therapists take advantage of the social aspects of music to obtain benefits for the patients, interacting with them musically, but this activity requires…

This paper presents an integrative review and experimental validation of artificial intelligence (AI) agents applied to music analysis and education. We synthesize the historical evolution from rule-based models to contemporary approaches…

Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for…

As generative models have risen in popularity, a domain that has risen alongside is generative models for music. Our study aims to compare the performance of a simple Markov chain model and a recurrent neural network (RNN) model, two…

Polyphonic music generation is still a challenge direction due to its correct between generating melody and harmony. Most of the previous studies used RNN-based models. However, the RNN-based models are hard to establish the relationship…

Kansei models were used to study the connotative meaning of music. In multimedia and mixed reality, automatically generated melodies are increasingly being used. It is important to consider whether and what feelings are communicated by this…

Generative AI models are increasingly being integrated into human task workflows, enabling the production of expressive content across a wide range of contexts. Unlike traditional human-AI design methods, the new approach to designing…

The co creativity community is making significant progress in developing more sophisticated and tailored systems to support and enhance human creativity. Design considerations from prior work can serve as a valuable and efficient foundation…

The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline" mode, with few restrictions on the processing time.…

Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in…

This study presents a novel Multi-Modal Graph Neural Network (MM-GNN) framework for socially aware music recommendation, designed to enhance personalization and foster community-based engagement. The proposed model introduces a fusion-free…

Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative…

Recommendation systems have become essential in modern music streaming platforms, due to the vast amount of content available. A common approach in recommendation systems is collaborative filtering, which suggests content to users based on…

Computing students increasingly rely on generative AI tools for programming assistance, often without formal instruction or guidance. This highlights a need to teach students how to effectively interact with AI models, particularly through…

Although the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight…

With the rise of artificial intelligence in recent years, there has been a rapid increase in its application towards creative domains, including music. There exist many systems built that apply machine learning approaches to the problem of…

Dynamic systems have found their use in sound synthesis as well as score synthesis. These levels can be integrated in monolithic autonomous systems in a novel approach to algorithmic composition that shares certain aesthetic motivations…

We introduce anticipation: a method for constructing a controllable generative model of a temporal point process (the event process) conditioned asynchronously on realizations of a second, correlated process (the control process). We…

Despite advances in deep algorithmic music generation, evaluation of generated samples often relies on human evaluation, which is subjective and costly. We focus on designing a homogeneous, objective framework for evaluating samples of…