Development of an AI-Powered Audio Content Analysis and Music Annotation System

The client faces significant time and resource constraints in manual parsing, creating delays in music production workflows, and requires high accuracy in detecting and extracting musical chords and notes from audio or video inputs. Lack of diverse, high-quality source data, coupled with limitations in processing speed and hardware resources, hampers efficiency.

A large-scale music production or content analysis company seeking automated chord and note detection from audio/video sources to streamline music editing, parsing, and copyright adherence.

• Develop a reliable machine learning-based algorithm capable of detecting and extracting chords and notes from input audio/video with an accuracy of at least 90%.
• Implement real-time processing capabilities to synchronize detected musical elements with source inputs efficiently.
• Optimize data processing to reduce analysis time from hours to minutes, ensuring scalability and quick turnaround.
• Create a robust neural network architecture that maintains high performance within hardware limitations such as GPU constraints.

• Audio/video input processing module capable of handling diverse data formats.
• Chord and note detection engine using a multi-layer neural network architecture trained on extensive labeled datasets.
• Synchronization module to align detected musical elements with source media in real time.
• Data augmentation routines (e.g., pitch shifting across ±6 semitones) to improve model robustness.
• Evaluation metrics integrated to measure accuracy against standards such as Mirex points.

• Audio/video data sources for input processing
• Existing media management or production workflows
• Evaluation tools for accuracy validation (e.g., datasets similar to JamStack)

• Achieve processing speeds that reduce analysis from hours to minutes for large datasets.
• Maintain detection accuracy above 90%, targeting 85-95% within operational parameters.
• Ensure system scalability to handle increasing data volumes and diverse input formats.
• Operate efficiently within hardware constraints, optimizing GPU utilization.

The implementation of this AI-based musical content analysis system is expected to significantly reduce processing time, enhance accuracy of chord and note detection, and improve overall workflow efficiency. This automation will enable the client to stay competitive in the fast-paced music industry, with anticipated detection accuracy of 85-95%, streamlining production processes and elevating the quality of musical parsing and rights management.