Artificial Musical Intelligence: A Survey
- URL: http://arxiv.org/abs/2006.10553v1
- Date: Wed, 17 Jun 2020 04:46:32 GMT
- Title: Artificial Musical Intelligence: A Survey
- Authors: Elad Liebman and Peter Stone
- Abstract summary: Music has become an increasingly prevalent domain of machine learning and artificial intelligence research.
This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit.
- Score: 51.477064918121336
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computers have been used to analyze and create music since they were first
introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the
Internet and large scale platforms for music recommendation and retrieval have
made music an increasingly prevalent domain of machine learning and artificial
intelligence research. While still nascent, several different approaches have
been employed to tackle what may broadly be referred to as "musical
intelligence." This article provides a definition of musical intelligence,
introduces a taxonomy of its constituent components, and surveys the wide range
of AI methods that can be, and have been, brought to bear in its pursuit, with
a particular emphasis on machine learning methods.
Related papers
- Improving Musical Instrument Classification with Advanced Machine Learning Techniques [0.0]
Recent advances in machine learning, specifically deep learning, have enhanced the capability to identify and classify musical instruments from audio signals.
This study applies various machine learning methods, including Naive Bayes, Support Vector Machines, Random Forests, Boosting techniques like AdaBoost and XGBoost.
The effectiveness of these methods is evaluated on the N Synth dataset, a large repository of annotated musical sounds.
arXiv Detail & Related papers (2024-11-01T00:13:46Z) - A Survey of Foundation Models for Music Understanding [60.83532699497597]
This work is one of the early reviews of the intersection of AI techniques and music understanding.
We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
arXiv Detail & Related papers (2024-09-15T03:34:14Z) - 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) - Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
Predictive coding (PC) has shown promising performance in machine intelligence tasks.
PC can model information processing in different brain areas, can be used in cognitive control and robotics.
arXiv Detail & Related papers (2023-08-15T16:37:16Z) - AI-Based Affective Music Generation Systems: A Review of Methods, and
Challenges [0.0]
Artificial intelligence-based approaches have become popular for creating affective music generation systems.
Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation systems may have a significant impact.
arXiv Detail & Related papers (2023-01-10T11:08:39Z) - A Survey on Artificial Intelligence for Music Generation: Agents,
Domains and Perspectives [10.349825060515181]
We describe how humans compose music and how new AI systems could imitate such process.
To understand how AI models and algorithms generate music, we explore, analyze and describe the agents that take part of the music generation process.
arXiv Detail & Related papers (2022-10-25T11:54:30Z) - Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
Deep learning has become its own subfield of machine learning.
In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
arXiv Detail & Related papers (2021-05-29T21:02:44Z) - 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) - Research on AI Composition Recognition Based on Music Rules [7.699648754969773]
Article constructs a music-rule-identifying algorithm through extracting modes.
It will identify the stability of the mode of machine-generated music to judge whether it is artificial intelligent.
arXiv Detail & Related papers (2020-10-15T14:51:24Z) - From Artificial Neural Networks to Deep Learning for Music Generation --
History, Concepts and Trends [0.0]
This paper provides a tutorial on music generation based on deep learning techniques.
It analyzes some early works from the late 1980s using artificial neural networks for music generation.
arXiv Detail & Related papers (2020-04-07T00:33:56Z)
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.