Music, a uniquely human domain with artistic and aesthetic value, has long been at the forefront of human endeavor. Following the digital transformation, it has reached an industrial inflection point. This article examines the further industrial changes being wrought by AI technology. We discuss the inherent industrial utility and future value of music in the AI ​​era.

Since the mid-2010s, the introduction of deep learning-based TTS models, or deep learning techniques, into the TTS field has brought about significant changes.
Google's 'Tacotron' series and DeepMind's 'WaveNet' are considered representative models.
The Tacotron series implements an end-to-end model system that converts text input into an intermediate representation such as a Mel spectrogram (a visualization of how the pitch, timbre, and intensity of a sound change over time, used in various fields such as speech recognition, music information retrieval, and audio analysis), and then restores it to speech, enabling more natural and flexible speech synthesis.
--- From "04_AI and Vocal, Voice Synthesis Technology"

It is clear that AI-powered music education is revolutionizing traditional music education methods through its advantages of personalized learning, improved learning efficiency, support for music creation, and real-time feedback.
Moreover, AI tools are economical and accessible, playing a positive role in promoting the universalization of music education.
However, AI-based music education lacks emotional feedback and still has limitations in guiding improvisation and creative expression.
Additionally, there is a high possibility of technical errors and there are aspects of the role of human teachers that cannot be replaced.

--- From "06_AI and Music Education"