Course 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.
Modules:
Module 1: Introduction to AI and Sound
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
Module 2: Harnessing AI Across Audio Domains
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
Module 3: Machine Learning and AI for Audio
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
Module 4: Speech Recognition and Text-to-Speech
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
Module 5: Audio Enhancement & Noise Reduction
5.1 Common Audio Issues
5.2 AI-based Noise Filtering & Enhancement
5.3 Use Case: 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
Module 6: Emotion & Sentiment Detection from 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
Module 7: Ethical and Privacy Considerations
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
Module 8: Advanced Applications & Future Trends
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