TrackSplit: audio analysis and export of individually tagged tracks
MY ROLEProject lead, software architect, Swift developer and UI designer
As project lead, software architect, Swift developer and UI designer, I developed a local audio editor that analyses long recordings and exports them as named individual tracks with metadata.
Streaming waveform analysis and detection of quiet passages generate suggested cuts that can be adjusted and previewed in a zoomable AppKit waveform. Track lists and CUE files supply titles and timings.
AVFoundation and Accelerate handle audio processing and analysis. Export supports FLAC, ALAC, AIFF, WAV and AAC, including metadata and cover artwork. Lossless output preserves the audio data when the bit depth is unchanged. Project files store edits and an authorised reference to the recording. Processing takes place locally.
Result: Implemented audio analysis, the editing model, CUE and track-list parsers, metadata and audio export in a tested Swift package core. Developed a SwiftUI/AppKit editor with playback and project management. Submitted version 1.0.0 for Mac App Store review.