AI+ Audio™

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

Certificate Code: AP 7010

About This Course

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

Certificate Overview

Included

Included Instructor-led OR Self-paced course + Official exam + Digital badge

Duration

Instructor-Led: 1 day (live or virtual)
Self-Paced: 8 hours of content

Prerequisites

Requires basic programming knowledge in Python, familiarity with audio signal processing and machine learning concepts, comfort with linear algebra and probability, and hands-on experience using DAWs or audio software. A creative and experimental mindset is essential.

Exam Format

50 questions, 70% passing, 90 minutes, online proctored exam

Course 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 & 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