Opleiding: AI+ Audio Practitioner™ eLearning

Formerly known as 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: Creative, Practical, Transformative
• Beginner-Friendly Learning: Perfect for newcomers eager to explore AI-powered audio, covering essential concepts with ease
• Comprehensive Skill Building: Includes speech processing, sound enhancement, voice synthesis, and real-world audio AI applications
• Industry-Ready Expertise: Understand how AI is reshaping music, media, entertainment, and communication sectors
• Hands-On Direction: Provides practical frameworks and guided exercises to help you create, analyse, and optimise audio using AI

Aspiring Audio Engineers – Ideal for those looking to integrate AI into sound design, mixing, and mastering.

Music Producers and Composers – Perfect for creators who want to use AI tools for music generation and adaptive composition.

Machine Learning Enthusiasts – Great for learners eager to apply ML models to audio analysis and synthesis.

Game and Media Developers – Suitable for professionals aiming to create intelligent, immersive, and responsive sound environments.

Tech Innovators and Researchers – Designed for individuals exploring cutting-edge AI applications in audio technology and digital sound innovation.

• Revolutionizes Sound CreationLearn how AI automates composition, mixing, and mastering, making audio production faster and more innovative.
• Enhances Audio QualityUse AI tools to clean, balance, and optimize sound for professional-grade results across platforms.
• Personalizes Listening ExperiencesDiscover how AI tailors music and soundscapes to individual preferences in real time.
• Bridges Creativity and TechnologyCombine artistic vision with AI-driven tools to create immersive, next-generation audio experiences.
• Expands Career OpportunitiesGain industry-ready skills for roles in music tech, sound design, gaming, and multimedia production.

• 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

• TensorFlow Audio Recognition
• PyTorch Sound Classification
• Librosa
• OpenAI Jukebox
• Google Magenta Studio
• Audacity AI Plugins
• Adobe Podcast AI Tools
• AIVA
• Wav2Vec
• SpeechBrain
• JUCE Framework
• FL Studio with AI Integrations
• Logic Pro Smart Tools
• Sonible Smart EQ
• Spotify Audio Analysis API
• NVIDIA Riva Speech SDK
• Deep Learning for Audio Toolkit
• AudioLDM
• Sound Design Automation Tools

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.