Graphical interface-based automated music composition use among elementary school students

Abstract

To facilitate music composition among children and students, this study examined the properties of automated music composition, attempting to leverage this advanced technique to provide children with an easy-to-learn and easy-to-accomplish method of composing music. Consequently, a graphical interface-based automated music composition (GBAMC) software program was developed, yielding enhanced usability, sequenced design, graphical technology, auditory learning, and intelligence. Learning satisfaction and attitude were surveyed among sixth-grade elementary school students (age = 12 years), indicating that students typically exhibited high levels of learning satisfaction and a positive learning attitude; learning satisfaction and attitude were also positively and significantly correlated in each dimension. Finally, recommendations were proposed for future studies to help users enjoy music composition without requiring an extensive background in musical theory.

Keywords

Automated music composition graphical interface-based automated music composition (GBAMC)learning satisfaction learning attitude sixth-grade elementary school students

Introduction

As an alternative to using conventional paper-and-pencil composition techniques, computer software and other computerized technologies should be developed and examined for potential in the creative process. However, Wiggins (2007) indicated that learning music involves applying knowledge, and teaching facilitates this process. Thus, music education comprises “teaching strategies that promote the thinking and understanding of music” (Barrett, 1998). A composer must grasp various relevant concepts and know how create music; when these key elements are integrated, composers can create pieces that express their understanding of music (Kaschub & Smith, 2009). Music education allows students to experience meaningful sounds, elucidating the feeling, creativity, and meaning at the core of music (Reimer, 2003), which is the art of human spiritual sustenance. Dancing, experimenting, and creating based on a rhythm enhances imagination, enriching the soul and facilitating an expanded consciousness.

The primary music teaching methods of Dalcroze, Orff, Kodály, and Suzuki highlight the importance of feeling music when cultivating self-realization and musical spirit among children:

The core essence and effect of the Dalcroze method: physical music thinking and presentation (Seitz, 2005) are key to awakening internal feelings toward music (Parker, 2002);

The Orff method: it is human nature to create and people exert their potential and attain success by pursuing continual exploration, improvisation, and creation (Volz, 2005);

The Kodály methodology: skills are developed in individual and group singing; in addition to building specific musical skills, this allows children to practice and sharpen their individual attention, learning, teamwork, and sensitivity to the group (Houlahan & Tacka, 2008);

The Suzuki method: music enhances human minds, opens hearts, and eliminates personal prejudices. Furthermore, instruction in musical instruments can cultivate moral character among children (Göktürk, 2008).

Studies of music education for children have recently grown in popularity, exploring improvisation and composition (Burnard, 2000), computer-based technology (Webster, 2007), and popular music (Green, 2006). Music education software can be applied to developing musical creations; this process is closely related to the cultural and creative industries. Bott, Crowley, and LaViola (2009) presented a novel 3D gesture interface for use in composition software, and Elsdon (2007) suggested that composition be performed using mobile devices. Music creation software is critical in the aforementioned industries and in child education; computerized music technologies, such as automated composition, exhibit potential to support its development. Huang (2008) reported that algorithmic composition (AC) can combine existing Windows-based program designs and interactive human-machine interfaces by using MIDI note transmission and music parameterization functions, minimizing the degree of composer intervention when using computers to compose music.

Recent literature regarding AC has primarily focused on developing musical accompaniment and appreciation systems; certain studies have integrated these among other fields (e.g. mechanical and interactive devices), but studies of composition instruction are rare. Azzara (2002) noted that developmental trend in improvisation was beginning education at age 12; however, Golver (2000) suggested music composition strategies should be instructed from ages 7–11.

Most music composition education for children is based on the score, rather than active listening. Kaschub and Smith (2009) explained the theory of music composition education for children, and Kaschub (1997) reported that students who composed gained enhanced awareness of score and an increased level of commitment to accurately interpreting the intentions of other composers. Mota et al. (2008) explored music composition education among children ages 10–13, using Hyperscore to convert visual representations; this required no musical training. In the current study, children used the proposed method to compose music based on rhythm, pitch, and chord settings and were supported using AC and graphical interface-based automated music composition (GBAMC). Rather than traditional score-based training, intuitive, listening-based methods of music composition warrant further investigation.

Incorporating automated composition to teach musical creation

Information technology (IT) is incorporated into teaching music composition. Information is obtained after processing raw data, such as texts, symbols, graphs, sounds, and images, by using a systematic procedure; IT typically refers to knowledge and skills in the design, manufacture, operation, application, and maintenance of computer software and hardware systems. IT is widely applied in the industrial, commercial, financial, defense, educational, leisure, entertainment, and scientific technology industries; its effect is omnipresent, profound, and widespread. For instance, Chen (2012) investigated the integration of IT and music education in Taiwan. Ho (2004) reported that IT caused paradigm shifts in teaching and enhanced music education curricula in Hong Kong.

If appropriate tools are selected for musical creation and a supportive environment is provided, then children discover musical presentation and a space for imagination in a world rich with sound (Gordon, 2003). Furthermore, Addessi and Pachet (2005, 2006) observed children aged 3–5 who interacted with an interactive musical system (Continuator) that allowed them to produce music in a way similar to playing the keyboard.

This study comprises discussions of the pedagogically grounded and research-based design of a technology-enhanced learning tool, the Music Paint Machine (Nijs, Moens, Lesaffre, & Leman, 2012) This interactive music system facilitates a musical experience in which a musician creates a digital painting by playing an acoustic musical instrument and moving his or her body on a colored pressure mat. As a learning tool, the Music Paint Machine was designed to develop musical creativity, stimulate musical understanding, encourage improvisation, and engender bonds with musical instruments.

By using MIDI note transmission and music parameterization functions, AC minimizes the degree of intervention required when using computers to compose music. People who lack a music background can compose music by entering musical parameters that automatically generate various styles of digital music. Currently, numerous AC methods are available that automatically complete music compositions, including artificial intelligence and neural network algorithms; among various techniques, “grammar of the musical genre” is the primary method (Huang, 2008). Table 1 lists a comparison of AC and MIDI sequencers.

Table 1.

Comparison of automated composition and MIDI sequencer.

	Automated composition	General MIDI sequencer
Harmony	Generated automatically	Created and matched by composers
Style	Automatic matching after selection	Analyzed and created by composers
Interface	Simple and convenient	Complex
Function	Limited by automation	Unlimited; can be used freely
User friendly	Relatively high	Relatively low
Foundation of music theory	Relatively little background knowledge in music theory required	More extensive background knowledge in music theory required
Composition complexity	Simpler, as limited by automated composition	From simple to complex

Note. Source of information: compiled by the authors.

Since 1956, various types of AC software have been developed and their functions differ based on their research and development orientation; these programs include Band-in-a-box, Cine Score, CWEST, IRCAM’s MusicLab 1/2, MAX/MSP, and Sonic Fire. However, this software cannot be applied to teaching children; therefore, the authors developed the GBAMC software, applying automated harmony generation and accompaniment functions to facilitate musical imagination.

Method

Students require no high-level skills in musical instruments when being educated regarding music composition; however, practical intermediate tools may be required during teaching (Jennings, 2006). In this study, students created rhythms and tunes by themselves; therefore, GBAMC software was developed to enable automatic chord configuration and musical arrangement. The students created a musical segment (eight sections) to improve their musical recognition, affection, and skills. The primary points of the proposed GBAMC include the following:

A. Usability

The interface and system were designed to facilitate usability, but the assessment indicators are similar. Folmer, Gurp, and Bosch (2003) organized the opinions of various scholars and referenced the international standard ISO 9126, selecting four frequently used evaluation indicators: learnability, efficiency of use, reliability of use, and satisfaction.

B. Programmed design

To provide users with a simple and easy-to-use graphical interface for musical composition, the software design included inputs for rhythm (as the foundation of melody) → theme melody (chords and arrangement can be completed in synchrony by using AC and can be manually adjusted) → functional chords (to enhance the music) → timbre, accompaniment method, and tonality (can be selected) → output (listen to the music).

C. Image science

The more readily available the required intuitive tools are, the more easily users can apply these tools. Typical digital tools (particularly digital musical tools) frequently involve graphs, videos, and other media to facilitate cross-field works (Rudi, 2007).

D. Ear-based learning

A critical principle of music teaching is “thinking with sound.” Guiding students to think with sound and perform aesthetic judgments yields effective learning outcomes (Hsieh, 2005). Stimulation with sound facilitates creative inspiration, allowing students to inspect the content of works and gain a sense of creative achievement (Murray, 1976).

E. Intelligent devices

When attempting to attain specific objectives, mistakes can cause major obstacles; thus, management strategies include prevention and restoration. Interfaces that exhibit effective prevention guide users to avoid making mistakes, whereas effective restoration allows users to restore their information following small errors, preventing catastrophic errors from occurring. Therefore, software must include intelligent devices that prevent errors to provide a free creative space that involves correct specifications.

During the software development process, the software architecture was divided into music generation and interface settings, each of which were independently tested to determine whether they conformed to the aforementioned developmental foci. After testing, the music generation and interface setting programs were integrated, and a final overall software test was conducted (Figure 1).

Figure 1.

The software development flow chart.

The first AC program was designed using algorithms. For example, to compose the melody for “Tin Pan Alley,” Klein and Bolitho of Burroughs, Inc. used the DATAtron computer research and development program, completing the music by using random selection and specific conditions. Subsequently, on 15 July 1956, “Push Button Bertha” was played in the KABC TV station in Los Angeles, California, US. During the same period, Hiller and Isaacson (1959) used an ILLIAC computer to initiate a series of composition “experiments,” releasing numerous works in a widely publicized recital. In addition using random settings, similar to Klein and Bolitho, Hiller and Isaacson applied the principle of Markov chains (Ames, 1987).

Equation (1) shows that Markov chains allow generating automated music styles by using probability control (Nierhaus, 2009), where a random variable X is present at an independent time t, the probability of the future condition X_t₊₁ (the random variable X at the t+1 time point) depends on the current condition X_t, and the probability of the music parameter at a certain time t_m and t_m₊₁ is:

P (X_{t m + 1 = j} / X_{t m = i}) = p_{i j} (t_{m}, t_{m + 1})

(1)

In addition to Markov chains, composers used complex theories related to mathematics, physics, and other fields such as chaos theory, probability applications, gene evolution, fractal theory, or algorithms developed by composers (Edwards, 2011); diverse AC software programs have been developed to calculate and control various elements, and their applications in musical creation differ based on their research and development orientations. Several types of AC software are alphabetically listed as follows to compare their functions and characteristics:

Band-in-a-box: provides diverse, rich accompaniment styles, allowing melodies to be edited;

CineScore: focuses on rhythmic and popular music styles and is suited for use in commercial music;

CWEST: focuses on the country style of American country music; music is generated by selecting parameters;

IRCAM’s MusicLab 1/2: is an educational application that allows music teachers to develop new pedagogical approaches by using a computer and computer-aided composition environments, providing computational possibilities and enhancing compositional knowledge;

MAX/MSP: processes MIDI in a graphical interface programming environment, and functions in combination with interactive devices; some applications can be generated using MAX/MSP and random data and probability control functions to perform AC (Didkovsky & Crawford, 2007);

Sonicfire: primarily used to generate background music for videos; it modifies existing music composition modules to complete works.

Other software programs, such as Morton Subotnick’s Making Music, can be used to generate music based on variations in the lines drawn using a game-like interface (Rosenboom, 1996).

GBAMC software operation

In this study, the authors developed and tested proposed GBAMC software, which was constructed using a JAVA platform. Various conditions were set to assist in generating chords and accompaniment when composing music.

Most music composition software programs, such as Cakewalk Sonar, Cubase, and Nuendo facilitate effective professional music composition; however, these programs require a substantial background in music theory, including keyboard skills and harmony. The proposed GBAMC is designed to allow non-musician children to compose music by using an intuitive, simple, and listening-based method that requires no advanced ability in music theory.

The operation of the GBAMC software is described as follows:

The initial interface: Figure 2 shows the initial interface of the GBAMC program. The upper columns are used for file management and playback, the left columns are used for music parameter setting options, and the right columns are divided into seven sections from top to bottom (positions for inputting pitch, speed, tonality, melody timbre, accompaniment timbre, accompaniment method, and drum kit timbre).

Rhythm-setting interface: Figure 3 shows the tempo-setting function of the GBAMC interface. For example, when using quadruple time, on the bottom of the right column, the vertical solid and dotted lines are divided into four intervals; each interval is additionally divided into four intervals (grey dotted lines); thus, a bar comprises 16 intervals and each interval represents a “quarter beat.” A rhythm point can be added by placing the cursor on a solid or dotted line and clicking the left mouse button; clicking the right mouse button deletes a rhythm point. The rhythm can be played and the content can be modified by clicking the “playback rhythm” button on the upper column after the rhythm is set.

Pitch-setting interface: Figure 4 shows the pitch-setting interface in the proposed GBAMC. In the middle of the right column, the vertical solid and dotted lines have the same meaning as they do in rhythm setting; the horizontal dotted lines represent the first notes of scales, such as do in the natural scale, and lines at distinct heights represent various pitches of do, or do at high pitch. A pitch generated is set according to the content of the scale. The point at which each pitch appears is set according to rhythm pairing based on the circles that display the rhythm setting; a strip that represents a melody is generated by clicking on pitch setting. Changing the time of appearance requires returning to the rhythm interface to edit before continuing to the pitch interface. Additionally, clicking “generate chords” after setting the pitch allows the pitch of that bar to be automatically determined and suitable chords are selected and displayed at random. The melody can be played, and the pitch can be modified by clicking the “playback” button on the upper column after the pitch is set.

Other settings: the speed, tonality, melody timbre, accompaniment timbre, accompaniment method, and drum kit timbre can be set at the beginning of a session or modified after music is created. The speed is set using a horizontal bar; moving the bar toward the left slows the tempo, whereas moving it toward right speeds the tempo. The tonality can be raised from a semitone to seven whole tones. The options available for the melody and timbre of accompaniment include piano, guitar, electric guitar, stringed instruments, brass instruments, harp, and pan flute. The accompaniment method can be chosen from a drop-down menu; nine accompaniment methods are currently available. Nine rhythms are presently available for the drum kit timbre and new functions will be added as the software becomes matures. The GBAMC software operating procedures are described as follows (Figure 5):

Initiate GBAMC, enter software interface

Open a file

Perform rhythm creation (set rhythm)

Preview rhythm, and return to Step 3 if unsatisfied

Perform melody creation (set pitch)

Preview pitch, and return to Step 5 if unsatisfied

Generate and select chords

Set speed, tonality, melody timbre, accompaniment timbre, accompaniment, and drum kit timbre

Playback the music, and return to Steps 3, 5, 7, or 8 if unsatisfied

Save the file, thereby completing the musical composition

Figure 2.

The initial interface of GBAMC.

Figure 3.

Rhythm setting in GBAMC.

Figure 4.

Pitch setting in GBAMC.

Figure 5.

The operating flow chart of the GBAMC software.

Twelve-year-old students were instructed 2 hours per week for 5 weeks to perform software-based composition. In this experiment, we prepared one computer per child in the classroom. Before the test, the authors conducted a 30-minute instruction session to ensure that the students understood the system. The experimental time was approximately 120 minutes. The presentation to the students primarily comprised a computer playback of the automatically composed music, including the automatically generated rhythm, pitch, and chords; the students determined whether they wanted to restart the procedure or retain the generated music. After Week 3, the children completed a questionnaire survey.

Learning satisfaction and attitudes of students after using GBAMC

Because AC is suited for teaching composition to sixth-grade students who are 12 years old, the students in three sixth-grade classes of an elementary school in Taoyuan County were recruited to determine whether they would accept the proposed software. Compared with the Suzuki teaching method (Göktürk, 2008), the proposed GBAMC yields a new and more efficient method of composing music, requiring no musical instrument or music training. Unlike other methods of teaching children improvisation and composition (Burnard, 2000), computer-based technology (Webster, 2007), and popular music (Green, 2006), the proposed GBAMC simply generates rhythm, pitch, and chords, facilitating listening-based composition.

The training comprises reaction, learning, behavior, and results, which are based on evaluations (Kirkpatrick, 1979). In the current study, the first two levels of training—learning (learning satisfaction) and behavior (learning attitude)—were used as the primary concerns regarding software validation.

The GBAMC software was incorporated into composition instruction, and a questionnaire was designed to survey the learning satisfaction and attitude of students. The proposed method is easy to learn and use; both rhythm and pitch interfaces can be readily operated by students while the software system automatically generates harmonic patterns. The questionnaire primarily assessed learning satisfaction and attitude. The difference between learning satisfaction and learning attitude is that learning satisfaction was used to assess the music creation instruction, whereas learning attitude was used to assess the persistence and consistency of students when learning composition.

Regarding learning satisfaction, the authors referenced input-output analysis, field theory, two-factor theory, differential theory, and class climate, summarizing the research orientations of and questions addressed by relevant literature. Learning satisfaction was divided into teaching materials, learning tools, instruction, and the needs of students. Learning attitude was divided into recognition, affective, and behavioral tendency based on the perspectives of Chang, Gagne, Li, and Hsu, organized by Wu (2009).

Table 2 lists the descriptive statistics regarding learning satisfaction among the 95 participants based on a 5-point Likert scale (1: strongly disagree, 2: disagree 3: neither agree nor disagree, 4: agree, and 5: strongly agree); the students scored 3.98, 3.57, 3.86, and 3.50 regarding teaching materials, learning tools, instruction, and the needs of students, respectively. The overall learning satisfaction score was 3.7228, indicating that students were satisfied in each aspect of music learning, particularly regarding teaching materials and instruction.

Table 2.

The degree of learning satisfaction of students with using GBAMC.

Dimension	Effective samples	Mean	SD	Skew
Teaching materials	92	3.98	.64	−.056
Learning tools	92	3.57	.78	−.367
Instruction	92	3.86	.73	−.251
Needs of students	91	3.50	.80	−.447
Overall	91	3.72	.56	−.234

Note. Source of information: compiled by the authors.

Table 3 lists the descriptive statistics regarding learning attitudes toward the proposed GBAMC; the students scored 4.0791, 3.5516, and 3.3750 on the cognitive, affective, and behavioral tendency components, respectively, indicating that students exhibit positive attitudes toward learning composition, particularly concerning recognition component. This signifies that students have generated cognitive attitudes regarding the importance and value of learning composition.

Table 3.

The learning attitudes of students with using GBAMC.

Dimension	Effective sample	Mean	SD	Skew
Cognitive component	91	4.08	.67	−.644
Affective component	92	3.56	.84	−.428
Behavioral tendency	90	3.38	.82	−.457
Overall	89	3.70	.60	−.589

Note. Source of information: compiled by the authors.

Each of the dimensions of student learning satisfaction and attitude were tested using a Pearson product-moment correlation (Table 4). The results indicate significant positive correlations among the four dimensions of learning satisfaction and the three dimensions of learning attitude. Therefore, incorporating the proposed GBAMC software in music composition education positively affects the learning satisfaction levels and attitudes of students.

Table 4.

The correlation among various dimensions in the degree of learning satisfaction and learning attitudes of students.

Dimension		Pearson correlation test	Learning satisfaction			Attitude toward learning
Dimension		Pearson correlation test	1	2	3	4	1	2
Learning satisfaction	Learning tool	Correlation coefficient Significance	.383 .000^**
	Teaching	Correlation coefficientSignificance	.410.000^**	.431.000^**
	Students’ needs	Correlation coefficientSignificance	.337.001^**	.376.000^**	.506.000^**
Attitude toward learning	Cognitive component	Correlation coefficientSignificance	.322.002^**	.243.020^*	.412.000^**	.557.000^**
	Affective component	Correlation coefficientSignificance	.311.003^**	.312.002^**	.263.011^*	.374.000^**	.425.000^**
	Behavioral tendency	Correlation coefficientSignificance	.377.000^**	.217.040^*	.304.004^**	.411.000^**	.374.000^**	.475.000^**

Note. ** Significant at the p < .01 level (two-tailed); * significant at the p < .05 level (two-tailed).

Conclusion

The most meaningful function of AC in music composition education is assisting learners to complete a creation that they may have not been to accomplish alone; this provides learning scaffolding and allows students to familiarize themselves with the musical content to be learned. To enhance the popularity of music, the proposed GBAMC AC software was developed, enabling students to experience the fun of composition. The surveyed sixth-grade students typically reported learning satisfaction and positive learning attitudes toward the music generated using GBAMC. However, further experiments and learning evaluations are required to determine whether the GBAMC software assists sixth-grade students in their cognition of and skills in music creation. The GBAMC software can be applied to sixth-grade students after it is further developed, and the results can be compared with those of existing teaching studies to verify its effectiveness.

The GBAMC software requires specific improvements to aspects including the intensity settings, ratio modification of the sound tracks, accompaniment method, and the diversity of available drum kits (future versions could include specific designs for particular music styles). Future studies should also consider the backgrounds of the surveyed participants; for example, various relevant teaching materials were modified to teach sixth-grade students. The authors discovered various errors and received valuable feedback from professional teachers regarding the proposed test. The proposed software can be modified to increase its practicality, allowing users enjoy music creation.

Footnotes

Funding

The authors appreciate the support of the National Science Council of Taiwan (NSC 101-2410-H-424 -014 -MY3).

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