AI+ Audio™
Experience the power of AI in Audio™ to reinvent music production, elevate sound design, and craft immersive auditory experiences.
Empower audio innovation with AI through this beginner-friendly course designed for learners eager to explore AI-powered audio technologies. The program builds comprehensive skills covering speech processing, sound enhancement, voice synthesis, and real-world audio AI applications. With an industry-focused perspective, participants gain insight into how AI is transforming music, media, entertainment, and communication sectors. Through hands-on direction, the course provides practical frameworks and guided exercises to help learners confidently create, analyse, and optimise audio using AI.
Enrollment Fee:
MUR 5,980
What You'll Learn
1.1 What is AI?
1.2 AI in Daily Life: Audio Examples
1.3 Basics of Sound Waves, Amplitude, Frequency
1.4 Digital Audio Fundamentals
2.1 AI for Audio Enhancement and Restoration
2.2 AI for Audio Accessibility and Personalization
2.3 AI in Speech and Voice Technologies
2.4 Popular Audio Libraries: Librosa, PyAudio
2.5 Use Case:AI-Driven Real-Time Captioning and Translation for Live Events
2.6 Case Study:Personalized Hearing Aid Adaptation Using AI and Smart Earbuds
2.7 Hands-on: Voice Emotion Detection using Deepgram’s Voice AI Platform
3.1 Machine Learning Models for Audio Applications
3.2 Deep Learning & Advanced AI Techniques for Audio
3.3 Audio-Specific Architectures: CNNs, RNNs, Transformers
3.4 Transfer Learning in Audio AI
3.5 Use Case: Speech-to-Text Transcription for Medical Records
3.6 Case Study: AI-powered Music Generation with Deep Learning
3.7 Hands-on: Build a Speech-to-Text Model Using TensorFlow
4.1 Fundamentals of Speech Recognition & Phonetics
4.2 API-based ASR Solutions
4.3 Building Custom ASR Models with Transformers
4.4 Introduction to TTS & Voice Cloning
4.5 Use Case: Automating Meeting Transcriptions with Google Speech-to-Text API
4.6 Case Study: Custom Transformer-based ASR Model for Multilingual Customer Support
4.7 Hands-on: Transcribe audio with an ASR API; generate speech from text
5.1 Common Audio Issues
5.2 AI-based Noise Filtering & Enhancement
5.3 Use Cases: Enhancing Audio Quality for Remote Work Calls Using AI Noise Reduction
5.4 Case Study: Krisp’s AI-powered Noise Cancellation in Podcast Production
5.5 Hands-on: Use Krisp or Adobe Enhance Speech to clean noisy audio
6.1 Introduction to Emotion Detection
6.2 AI Models for Emotion Detection: RNNs, LSTMs, CNNs
6.3 Challenges: Bias, Multilingual Contexts, Reliability
6.4 Use Case: Enhancing Customer Service with Emotion Detection from Speech
6.5 Case Study: IBM Watson Tone Analyzer for Real-Time Emotion Recognition
6.6 Hands-on: Use IBM Watson Tone Analyzer or similar APIs to analyze speech samples
7.1 Deepfakes and Voice Cloning Risks
7.2 Privacy and Data Security
7.3 Bias and Fairness in Audio AI
7.4 Use Case: Implementing Ethical Voice Data Collection and Consent Management
7.5 Case Study: Addressing Bias and Privacy in Audio AI under GDPR Compliance
7.6 Hands-on: Detect fake audio clips; create an ethical AI checklist
8.1 Sound Event Detection & Classification
8.2 Audio Search and Indexing
8.3 Innovations: Multimodal AI, Edge Computing, 3D Audio
8.4 Emerging Careers in Audio AI
Prerequisites
- Basic programming knowledge – Familiarity with Python or similar languages.
- Understanding of audio signal processing – Know fundamental audio manipulation techniques.
- Machine learning fundamentals – Basic knowledge of algorithms and model training.
- Mathematical proficiency – Comfort with linear algebra and probability concepts.
- Experience with audio software tools – Hands-on use of DAWs or similar tools.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 5,980