Categories AI + Business, Media

AI+ Audio™

  • Experience the power of AI in Audio™ to reinvent music production, elevate sound design, and craft immersive auditory experiences.

$82.00

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Intermediate
AI+ Audio.
24h
1 Year.
$82.00
Training 5 or more people?

Next-level music and sound design.

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

Course Curriculum

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 & 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 & 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 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

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

Learning Objectives

  • Learn AI Applications in Audio: Grasp how AI enhances technologies like speech recognition, emotion detection, and audio enhancement.
  • Develop Practical AI Skills: Apply AI-driven tools and APIs for real-world applications such as transcription and noise reduction.
  • Equip with Ethical Knowledge: Understand ethical considerations, data privacy, and bias mitigation in AI-driven audio technologies.
  • Grasp Real-World Audio AI Challenges: Navigate challenges in the AI + audio space, preparing for innovation and leadership roles.
  • Demonstrate AI + Audio Expertise: Be well-equipped to lead and innovate in industries combining AI and audio technologies.

Resource Center

  • Videos, Use cases, Workshops, E-books and tools.

Target Audience

  • Aspiring Audio Engineers.
  • Music Producers and Composers.
  • Machine Learning Enthusiasts.
  • Game and Media Developers.
  • Tech Innovators and Researchers.

Job Roles & Industry Outlook 

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Al Sound Designer
Design Al-driven soundscapes, automate mixing and mastering processes, and generate adaptive audio for games, films, and virtual environments.
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Audio Technology Manager
Lead the integration of Al tools in music production, post-processing, and sound engineering to streamline workflows and boost creative output.
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Chief Audio Innovation Officer (CAIO)
Drive Al transformation in the audio industry by championing intelligent sound design, personalized listening technologies, and next-generation auditory innovation.
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Al Audio Engineer
Develop intelligent sound systems that adapt to user environments, enhance audio quality, and create dynamic, immersive listening experiences across platforms.
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Audio Data Scientist
Analyze sound data to build predictive models for music recommendation, voice recognition, and personalized audio experiences.

Exam Information

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Duration
90 minutes
Passing Score Icon-71608a
Passing Score
70% (35/50)
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Format
50 multiple-choice/multiple-response questions
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Delivery Method
Online via proctored exam platform (flexible scheduling)
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Fees include
One year of access to course content, including continuous updates. Certification exam. One exam re-take.

Exam Blueprint:

Introduction to AI and Sound – 7%
Harnessing AI Across Audio Domains – 15%
Machine Learning & AI for Audio – 15%
Speech Recognition & Text-to-Speech – 15%
Audio Enhancement & Noise Reduction – 12%
Emotion & Sentiment Detection from Audio – 12%
Ethical and Privacy Considerations – 12%
Advanced Applications and Future Trends – 12%

Explore our Schedules

Nov 15 - Nov 17
GMT 09:00 AM - 05:00 PM
In-person
In-Person
Doha
724.5$
3 Days
Sep 20 - Sep 22
GMT 09:00 AM - 05:00 PM
In-person
In-Person
Muscat
724.5$
3 Days
Jul 5 - Jul 12
GMT 06:00 PM - 10:00 PM
LVT
LVT
Zoom
280$
6 Days
Sep 20 - Sep 27
GMT 06:00 PM- 10:00 PM
LVT
LVT
Zoom
280$
6 Days
Nov 29 - Dec 6
GMT 06:00 PM- 10:00 PM
LVT
LVT
Zoom
280$
6 Days

Certificate of Completion

AI+ Audio.

AI-Healthcare-Certificate-Blurred 1

$82.00

ChartBar (1)-51bd46
Intermediate
AI+ Audio.
24h
1 Year.
$82.00
Training 5 or more people?

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