Stefania TOMASIELLO | INFORMATION TECHNOLOGIES FOR MUSIC PRODUCTION
Stefania TOMASIELLO INFORMATION TECHNOLOGIES FOR MUSIC PRODUCTION
cod. II22400018
INFORMATION TECHNOLOGIES FOR MUSIC PRODUCTION
| II22400018 | |
| DEPARTMENT OF INDUSTRIAL ENGINEERING | |
| EQF7 | |
| ELECTRONIC ENGINEERING | |
| 2025/2026 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2025 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 9 | 90 | LESSONS |
| Objectives | |
|---|---|
| THE COURSE AIMS AT INTRODUCING THE MAIN INFORMATION TECHNOLOGY APPROACHES FOR AUDIO SIGNALS. IN PARTICULAR, THE COURSE WILL INTRODUCE SOME MACHINE LEARNING TECHNIQUES FOR MUSIC INFORMATION RETRIEVAL. KNOWLEDGE AND COMPREHENSION SKILLS: AT THE END OF THE TEACHING ACTIVITIES, THE STUDENTS WILL BE ABLE TO UNDERSTAND THE MAIN ML TECHNIQUES FOR MUSIC INFORMATION RETRIEVAL. IN PARTICULAR, THEY WILL BE ABLE TO PICK THE PROPER APPROACHES FOR SOLVING REAL PROBLEMS. THE STUDENTS WILL ACQUIRE SKILLS RELATED TO THE USE OF THE PYTHON PROGRAMMING LANGUAGE FOR ML FOR GIVEN TASKS IN THE CONTEXT OF COMPUTER MUSIC PRODUCTION. APPLIED KNOWLEDGE AND COMPREHENSION SKILLS: AT THE END OF THE COURSE, THE STUDENTS WILL BE ABLE TO EDIT, COMPRESS, AND MODIFY THE AUDIO SIGNAL, AND TACKLE SOME PROBLEMS RELATED TO MUSIC INFORMATION RETRIEVAL USING APPROPRIATE ML TECHNIQUES (E.G. MUSIC GENRE RECOGNITION, CHORD RECOGNITION). AUTONOMY OF JUDGMENT: STUDENTS WILL BE ABLE TO IDENTIFY THE MOST APPROPRIATE APPROACHES TO OBTAIN THE BEST SOLUTION FOR SOLVING A GIVEN PROBLEM. COMMUNICATION SKILLS: AT THE END OF THE COURSE, THE STUDENTS WILL ACQUIRE THE BASIC TERMINOLOGY AND VOCABULARY OF ML IN THE CONTEXT OF MUSIC INFORMATION RETRIEVAL. LEARNING SKILLS: THE COURSE AIMS TO DEVELOP STUDENTS' LEARNING SKILLS SO THAT THEY WILL BE ABLE TO UPDATE THEIR KNOWLEDGE AND SKILLS INDEPENDENTLY. STUDENTS SHOULD BE ABLE TO APPLY THE ACQUIRED KNOWLEDGE TO UNEXPLORED CONTEXTS AND DEEPEN THE TOPICS COVERED USING MATERIAL AND LIBRARIES DIFFERENT FROM THOSE PROPOSED. |
| Prerequisites | |
|---|---|
| BASICS OF COMPUTER PROGRAMMING |
| Contents | |
|---|---|
| PART 1 (40 HOURS, OF WHICH 15 HOURS FOR PRACTICE SESSIONS) - RECALLING BASICS OF MUSIC THEORY - RECALLING BASICS OF SIGNAL PROCESSING (SIGNAL REPRESENTATION, SPECTRAL ANALYSIS) - SPECTROGRAM, CHROMAGRAM, MEL SCALE - EXPLORING SOME TOOLS (TUNEPAD, LIBROSA) PART 2 (50 HOURS, OF WHICH 30 HOURS FOR PRACTICE SESSION AND PROJECT) - INTRODUCING MACHINE LEARNING WITH PYTHON FOR MUSIC (MUSIC INFORMATION RETRIEVAL, AUTOMATIC MUSIC GENERATION) |
| Teaching Methods | |
|---|---|
| - LECTURES (45 H) - PRACTICE SESSIONS - STUDENTS WILL BE ASKED TO TACKLE, INDEPENDENTLY OR IN SMALL GROUPS, SOME RELEVANT PROBLEMS (20 H) - PROJECT WORK, RELATED TO THE MAIN COURSE (25 H) |
| Verification of learning | |
|---|---|
| THERE IS AN EXAM, CONSISTING OF THREE QUESTIONS, ONE OF WHICH IS RELATED TO THE PROJECT WORK. |
| Texts | |
|---|---|
| -J.P. BRIOT, G. HADJERES, F.D. PACHET, DEEP LEARNING TECHNIQUES FOR MUSIC GENERATION, SPRINGER (2019) -M. S. HORN, M. WEST, C. ROBERTS, INTRODUCTION TO DIGITAL MUSIC WITH PYTHON PROGRAMMING, ROUTLEDGE (2022) |
| More Information | |
|---|---|
| LECTURES IN ITALIAN. STUDY MATERIAL IN ENGLISH. |
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