| Course code | AIST2010 |
| Course title | Introduction to Computer Music: From Analysis to Algorithmic Music 電腦音樂導論：從分析到算法作曲 |
| Course description | This course aims to present an overview of computer music for students with basic programming abilities. The course starts with fundamental audio analysis and synthesis, and finally progress towards algorithmic music generation with machine learning. Hands-on exercises also cover software toolboxes for music information retrieval and programming. 本科為有基礎編程能力的學生而設，提供電腦音樂各方面的概覽。科目內容由聲音分析及合成開始，到最後以機器學習自動生成音樂。實際操作練習也涵蓋音樂資訊檢索軟件包，以及音樂編程等。 |
| Unit(s) | 3 |
| Course level | Undergraduate |
| Semester | 1 or 2 |
| Pre-requisites | CSCI1110 or CSCI1120 or CSCI1130 or CSCI1510 or CSCI1520 or CSCI1530 or CSCI1540 or ENGG1110 or ESTR1002 or ESTR1100 or ESTR1102 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning objectives | At the end of the course of studies, students will have acquired 1. knowledge in fundamental computer music concepts; 2. knowledge in sound analysis and synthesis; 3. ability for software and toolboxes for music information retrieval; 4. basic understanding of music generation with machine learning. |
| Learning outcomes | At the end of the course of studies, students will have acquired the ability to 1. Understand how audio is stored and manipulated in digital format; 2. Relate basic music related terms with programming concepts; 3. Perform analysis and synthesis for sounds; 4. Use a number of toolboxes and software for programming and music information retrieval. |
| Assessment (for reference only) |
Project: 40% Lab reports: 20% Essay test or exam: 20% Short answer test or exam: 20% |
| Recommended Reading List | 1. The Computer Music Tutorial, by Curtis Roads 2. The Theory and Techniques of Electronic Music, by Miller Puckette 3. The CSound Book: Perspectives in Software Synthesis, Sound Design, Signal Processing, and Programming, by Richard Boulanger 4. The Audio Programming Book, by Richard Boulanger and Victor Lazzarini |
| AISTN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate and solve AI-related engineering problems (K/S); | Y |
| 2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V); |
Y |
| 3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V); | Y |
| 4. communicate and work effectively in multi-disciplinary teams (S/V); |
|
| 5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S); |
Y |
| 6. design and conduct experiments, as well as to analyze and interpret massive data (K/S); | Y |
| 7. use the techniques, skills, and modern computing tools necessary for engineering practice appropriate to the AI and computing discipline (K/S); | Y |
| 8. understand professional and ethical responsibility (K/V); and | |
| 9. recognize the need for and the importance of life-long learning (V). |
Y |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes | |