Core Module Information
Module title: AI for Audio and Sound Design

SCQF level: 10:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: IMD10113
Module leader: Callum Goddard
School School of Computing, Engineering and the Built Environment
Subject area group: Applied Informatics
Prerequisites

Requisites: Pre-requisite: [Module INF09116] User-Centred Research Methods AND Pre-requisite: [Module IMD09148] Sound Design

Description of module content:

You will learn how to use the latest artificial intelligence technologies for audio and sound design. This module will introduce students to the concepts and the skills and knowledge to understand and use these new tools effectively in their ongoing practice. Students will learn how these technologies are developed using python, as well as how to choose and use deep learning models for audio and sound design related tasks. They will integrate these technologies into creative workflows and learning how to adjust and train for bespoke tasks and evaluate their effectiveness.

Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Understand the core principles and algorithms underpinning current AI models applied to audio tasks.

LO2: Develop competencies in python, git hub and deep learning python libraries.

LO3: Learn how task specific datasets are created and used to train AI models, including how datasets are split into training, testing and validation sets.

LO4: Explore the ethical issues in developing new AI algorithms, including the legality and ethics of datasets as well as energy consuption

LO5: Evaluate the performance of AI models on their trained task.

Full Details of Teaching and Assessment

Indicative References and Reading List - URL:
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