TORONTO — Mike Field Enterprises recently completed an AI-powered audio-analysis system that combines speech recognition, audio processing, and generative AI to automatically analyze the structure of recorded music.
Developed for New Zealand for Germans Inc., the project uses a multi-stage AI pipeline integrating PyTorch, AssemblyAI, Faster-Whisper, and the OpenAI API. Audio is processed through several stages for vocal isolation, transcription, timestamp analysis, and semantic identification of song sections such as verses, choruses, and bridges.
One of the engineering challenges was connecting AI-generated results back to the underlying musical structure. Python-based processing is orchestrated through a PHP and JavaScript application that aligns high-resolution timestamps with musical bars and presents the resulting song structure through an interactive visualization.
The project demonstrates how multiple AI technologies can be combined into a larger production workflow. Rather than relying on a single model or API, different components handle specialized tasks while conventional application code manages processing, data flow, synchronization, and the user experience.
For Mike Field, the project also brought together two areas of longstanding experience: software engineering and audio. The result is an example of applied AI in which machine learning, speech technology, APIs, and full-stack development work together to solve a specialized real-world problem.
ABOUT MIKE FIELD ENTERPRISES:
Mike Field Enterprises provides senior software engineering and AI architecture for enterprise applications, generative AI, agentic systems, intelligent applications, and full-stack software development. Mike Field is an AI architect and senior full-stack software engineer with 25 years of experience building production software and enterprise systems.