📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Sound Modification, from foundations to a certified capstone project.
Audio DSP
• Sampling, quantisation, aliasing and the constraints they impose
• Time-frequency representation: STFT, mel spectrograms and phase
• Loudness, dynamic range and perceptual measures that matter to listeners
Source Separation
• Music and speech source separation architectures
• Dereverberation, denoising and artefact trade-offs
• Evaluation with SDR and perceptual listening tests, not loss curves
Transformation
• Pitch and time manipulation without formant distortion
• Voice conversion and timbre transfer approaches
• Neural vocoders and the quality-versus-latency trade-off
Generation
• Text-to-speech and controllable prosody
• Generative audio and music models, and their controllability limits
• Real-time constraints for live and interactive use
Ethics
• Voice cloning consent, likeness rights and disclosure obligations
• Training-data provenance and rights in music and speech corpora
• Watermarking and synthetic audio detection, and their fragility
Earn government-registered certification in AI in Sound Modification
e-Certificate and e-Marksheet issued on successful completion.
