LEARNING OUTCOMES
On successful completion of this course, students will be able to:
- Develop a depth of understanding of the recent audio domain deep learning models, artificial intelligence (AI) methods and network architectures.
- Build AI applications for their own purposes.
- Prepare data sets and train deep learning models using cluster network in Aalto University.
- Develop a depth of understanding of the differences in input, computational cost and sonic characteristics between the different models
Credits: 3
Schedule: 31.01.2023 - 17.02.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Koray Tahiroglu
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Artificial intelligence (AI) methods have been the most recent trend for artists to experiment with advanced technologies. The growing use of AI in music to support musical creativity enables new possibilities of utilising new musical instruments and interfaces. In Deep Learning of Audio course, we will introduce students to the state of the art in deep learning models and AI methods for sound and music generation. The course will provide an overview of recent AI implementations such as, Google Magenta's (AI Duet, NSynth, GANSynth, DDSP) GANSpaceSynth and SampleRNN. There will be hands-on exercises on each course topic. We will provide code templates that integrate the functionality from open source deep learning audio projects into Pure Data programming environment. We will also provide detailed setup instructions and automated scripts to make installation of the required tools as easy as possible (for Pure Data, Python, Conda, Magenta, PyExt). Students will further explore a particular model and incorporate it into their own project work.
Assessment Methods and Criteria
valid for whole curriculum period:
Participation in teaching, completed assignments.
Minimum 80% attendance.
See MyCourses for more detailed information on evaluations methods and criteria.
Each student project work will be assessed with the following criteria: Design Values,
Aesthetics and Originality; UI design and Production Values; Code Design Quality;
Project Analysis - Depth of Understanding; Idea generation and implementation; and
Presentation style.
Workload
valid for whole curriculum period:
3 ECTS ≈ 81h
Contact 36h / 45h
The course consists of lectures, exercises, reading materials, tutoring individual or group works. Contact teaching hours are allocated for project work in the classroom and students receive sufficient guidance, feedback and tutoring.
Active participation in the course – interaction, QA (10%), Course assignments (10%)
Project Interim - Concept presentation (1 page)(10%) Project Final Presentation (15%)
Project Final Delivery of the Working Prototype including the video demonstration (40%)
Project Final Concept paper / learning diary (1-3 page) (15%)
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
4 Quality Education
10 Reduced Inequality
12 Responsible Production and Consumption
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III
2023-2024 Spring IIIEnrollment :
Minimum amount of participants: 8
Maximum amount of participants varies according to the implementation of the course.Registration for Courses: Sisu.
Priority order to courses is according to the order of priority decided by the Academic committee for School of Arts, Design and Architecture:
https://www.aalto.fi/en/services/registering-to-courses-and-the-order-of-priority-at-aalto-arts
- Teacher: Tahiroglu Koray