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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 19235

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Special Issue Editor

Dr. Philippe Esling

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Guest Editor

Institut de Recherche et Coordination Acoustique Musique (IRCAM), Paris, France
Interests: time series; multiobjective; computer music; signal processing; genomics

Special Issue Information

Computer music has been a flourishing and exciting field of study for more than half of a century. This domain entails a unique diversity in the scientific fields that it reaches. Indeed, it collides artistic, scientific, and technological research visions to generate, model, and comprehend music through electronic artefacts and mathematical theories. Doing so requires the development of methods from mathematics, computer science, signal processing, cognition, musicology, and psychology. The field of computer music has already had a tremendous impact on the contemporary and popular artistic scene, such as through the pervasive use of musical synthesizer technologies.

The field of computer music itself is constantly moving and posing new theoretical and technological challenges, with unique interdisciplinary considerations. As most fields, it now faces a renewed momentum in the wake of artificial intelligence and machine learning approaches, which play an increasingly prominent role in music composition, performance, and production. The use of such technologies for creativity appear in a natural continuity to the artistic trend of this century. Through the understanding of musical creativity, most of the current challenges in machine learning are found: the question of temporality, hierarchical structures, and the lack of a formal goal in creativity. These exciting issues of addressing the question of creative intelligence through music could give rise to a whole new category of generic creative learning systems.

We are inviting the submission of manuscripts to this Special Issue on “Advances in Computer Music.” This Special Issue aims to cover large aspects of computational creativity applied to music, both in the recent trends of artificial intelligence, machine learning, and generative models applied to music, but also recent cutting-edge innovations in more traditional fields of mathematical modeling, sound synthesis and transformation, innovative interfaces for music expression, computer-based music composition and analysis and any other scientific approach aiming to challenge and push forward the limits of human creativity in music.

Dr. Philippe Esling
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Computer Music
Automatic music generation and composition
Music performance and improvisation
Creative artificial intelligence
Machine learning applied to music
Learning or modeling music style and structure
Computer music languages and software
Computer-based music analysis
Transforming musical material
Sound synthesis and modification
Automatic synthesizer design
Adaptive music generation systems
Computational creativity for music
Computational musicology
Mathematical music theory
Music games and educational tools
Interactive performance systems
Novel interfaces for music expression
Music control and performance
Music cognition

Published Papers (7 papers)

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Research

25 pages, 1111 KiB

Open AccessArticle

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

by José López-Montes, Miguel Molina-Solana and Waldo Fajardo

Appl. Sci. 2022, 12(16), 8322; https://doi.org/10.3390/app12168322 - 19 Aug 2022

Cited by 1 | Viewed by 2222

Abstract

We present GenoMus, a new model for artificial musical creativity based on a procedural approach, able to represent compositional techniques behind a musical score. This model aims to build a framework for automatic creativity, that is easily adaptable to other domains beyond music. The core of GenoMus is a functional grammar designed to cover a wide range of styles, integrating traditional and contemporary composing techniques. In its encoded form, both composing methods and music scores are represented as one-dimensional arrays of normalized values. On the other hand, the decoded form of GenoMus grammar is human-readable, allowing for manual editing and the implementation of user-defined processes. Musical procedures (genotypes) are functional trees, able to generate musical scores (phenotypes). Each subprocess uses the same generic functional structure, regardless of the time scale, polyphonic structure, or traditional or algorithmic process being employed. Some works produced with the algorithm have been already published. This highly homogeneous and modular approach simplifies metaprogramming and maximizes search space. Its abstract and compact representation of musical knowledge as pure numeric arrays is optimized for the application of different machine learning paradigms. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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17 pages, 3108 KiB

Open AccessArticle

Comparison and Analysis of Acoustic Features of Western and Chinese Classical Music Emotion Recognition Based on V-A Model

by Xin Wang, Li Wang and Lingyun Xie

Appl. Sci. 2022, 12(12), 5787; https://doi.org/10.3390/app12125787 - 7 Jun 2022

Cited by 7 | Viewed by 2165

Abstract

Music emotion recognition is increasingly becoming important in scientific research and practical applications. Due to the differences in musical characteristics between Western and Chinese classical music, it is necessary to investigate the distinctions in music emotional feature sets to improve the accuracy of cross-cultural emotion recognition models. Therefore, a comparative study on emotion recognition in Chinese and Western classical music was conducted. Using the V-A model as an emotional perception model, approximately 1000 pieces of Western and Chinese classical excerpts in total were selected, and approximately 20-dimension feature sets for different emotional dimensions of different datasets were finally extracted. We considered different kinds of algorithms at each step of the training process, from pre-processing to feature selection and regression model selection. The results reveal that the combination of MaxAbsScaler pre-processing and the wrapper method using the recursive feature elimination algorithm based on extremely randomized trees is the optimal algorithm. The harmonic change detection function is a culturally universal feature, whereas spectral flux is a culturally specific feature for Chinese classical music. It is also found that pitch features are more significant for Western classical music, whereas loudness and rhythm features are more significant for Chinese classical music. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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22 pages, 1762 KiB

Open AccessArticle

A General Framework for Visualization of Sound Collections in Musical Interfaces

by Gerard Roma, Anna Xambó, Owen Green and Pierre Alexandre Tremblay

Appl. Sci. 2021, 11(24), 11926; https://doi.org/10.3390/app112411926 - 15 Dec 2021

Cited by 4 | Viewed by 2392

Abstract

While audio data play an increasingly central role in computer-based music production, interaction with large sound collections in most available music creation and production environments is very often still limited to scrolling long lists of file names. This paper describes a general framework for devising interactive applications based on the content-based visualization of sound collections. The proposed framework allows for a modular combination of different techniques for sound segmentation, analysis, and dimensionality reduction, using the reduced feature space for interactive applications. We analyze several prototypes presented in the literature and describe their limitations. We propose a more general framework that can be used flexibly to devise music creation interfaces. The proposed approach includes several novel contributions with respect to previously used pipelines, such as using unsupervised feature learning, content-based sound icons, and control of the output space layout. We present an implementation of the framework using the SuperCollider computer music language, and three example prototypes demonstrating its use for data-driven music interfaces. Our results demonstrate the potential of unsupervised machine learning and visualization for creative applications in computer music. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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18 pages, 3618 KiB

Open AccessArticle

Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks

by Wenming Gui, Yukun Li, Xian Zang and Jinglan Zhang

Appl. Sci. 2021, 11(24), 11838; https://doi.org/10.3390/app112411838 - 13 Dec 2021

Cited by 2 | Viewed by 2128

Abstract

Singing voice detection is still a challenging task because the voice can be obscured by instruments having the same frequency band, and even the same timbre, produced by mimicking the mechanism of human singing. Because of the poor adaptability and complexity of feature engineering, there is a recent trend towards feature learning in which deep neural networks play the roles of feature extraction and classification. In this paper, we present two methods to explore the channel properties in the convolution neural network to improve the performance of singing voice detection by feature learning. First, channel attention learning is presented to measure the importance of a feature, in which two attention mechanisms are exploited, i.e., the scaled dot-product and squeeze-and-excitation. This method focuses on learning the importance of the feature map so that the neurons can place more attention on the more important feature maps. Second, the multi-scale representations are fed to the input channels, aiming at adding more information in terms of scale. Generally, different songs need different scales of a spectrogram to be represented, and multi-scale representations ensure the network can choose the best one for the task. In the experimental stage, we proved the effectiveness of the two methods based on three public datasets, with the accuracy performance increasing by up to 2.13 percent compared to its already high initial level. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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22 pages, 31246 KiB

Open AccessArticle

Automatic Evaluation of Piano Performances for STEAM Education

by Varinya Phanichraksaphong and Wei-Ho Tsai

Appl. Sci. 2021, 11(24), 11783; https://doi.org/10.3390/app112411783 - 11 Dec 2021

Cited by 16 | Viewed by 3254

Abstract

Music plays an important part in the lives of people from an early age. Many parents invest in music education of various types for their children as arts and music are of economic importance. This leads to a new trend that the STEAM education system draws more and more attention from the STEM education system that has been developed over several years. For example, parents let their children listen to music since they were in the womb and invest their money in studying music at an early age, especially for playing and learning musical instruments. As far as education is concerned, assessment for music performances should be standardized, not based on the individual teacher’s standard. Thus, in this study, automatic assessment methods for piano performances were developed. Two types of piano articulation were taken into account, namely “Legato” with vibration notes using sustain pedals and “Staccato” with detached notes without the use of sustain pedals. For each type, piano sounds were analyzed and classified into “Good”, “Normal”, and “Bad” categories. The study investigated four approaches for this task: Support Vector Machine (SVM), Naive Bayes (NB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). The experiments were conducted using 4680 test samples, including isolated scale notes and kids’ songs, produced by 13 performers. The results show that the CNN approach is superior to the other approaches, with a classification accuracy of more than eighty percent. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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18 pages, 1193 KiB

Open AccessArticle

Emergent Interfaces: Vague, Complex, Bespoke and Embodied Interaction between Humans and Computers

by Tim Murray-Browne and Panagiotis Tigas

Appl. Sci. 2021, 11(18), 8531; https://doi.org/10.3390/app11188531 - 14 Sep 2021

Cited by 3 | Viewed by 2272

Abstract

Most Human–Computer Interfaces are built on the paradigm of manipulating abstract representations. This can be limiting when computers are used in artistic performance or as mediators of social connection, where we rely on qualities of embodied thinking: intuition, context, resonance, ambiguity and fluidity. We explore an alternative approach to designing interaction that we call the emergent interface: interaction leveraging unsupervised machine learning to replace designed abstractions with contextually derived emergent representations. The approach offers opportunities to create interfaces bespoke to a single individual, to continually evolve and adapt the interface in line with that individual’s needs and affordances, and to bridge more deeply with the complex and imprecise interaction that defines much of our non-digital communication. We explore this approach through artistic research rooted in music, dance and AI with the partially emergent system Sonified Body. The system maps the moving body into sound using an emergent representation of the body derived from a corpus of improvised movement from the first author. We explore this system in a residency with three dancers. We reflect on the broader implications and challenges of this alternative way of thinking about interaction, and how far it may help users avoid being limited by the assumptions of a system’s designer. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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19 pages, 4204 KiB

Open AccessArticle

Singing Transcription from Polyphonic Music Using Melody Contour Filtering

by Zhuang He and Yin Feng

Appl. Sci. 2021, 11(13), 5913; https://doi.org/10.3390/app11135913 - 25 Jun 2021

Cited by 2 | Viewed by 2346

Abstract

Automatic singing transcription and analysis from polyphonic music records are essential in a number of indexing techniques for computational auditory scenes. To obtain a note-level sequence in this work, we divide the singing transcription task into two subtasks: melody extraction and note transcription. We construct a salience function in terms of harmonic and rhythmic similarity and a measurement of spectral balance. Central to our proposed method is the measurement of melody contours, which are calculated using edge searching based on their continuity properties. We calculate the mean contour salience by separating melody analysis from the adjacent breakpoint connective strength matrix, and we select the final melody contour to determine MIDI notes. This unique method, combining audio signals with image edge analysis, provides a more interpretable analysis platform for continuous singing signals. Experimental analysis using Music Information Retrieval Evaluation Exchange (MIREX) datasets shows that our technique achieves promising results both for audio melody extraction and polyphonic singing transcription. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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