Chord Reaper

AI-powered audio analysis for beat detection, chord recognition, and lyrics synchronization. Powered by state-of-the-art machine learning models.

Capabilities

Chord Reaper analyzes audio to extract musical information using advanced deep learning models.

Beat Detection

Identify beat positions, downbeats, BPM, and time signatures using neural network models.

Chord Recognition

Recognize chord progressions with 301 chord labels using CNN-LSTM architecture.

Lyrics Synchronization

Fetch and display time-synced lyrics from LRClib and Genius databases.

Song Structure

Segment songs into structural sections (verse, chorus, bridge) using SongFormer.

Models & Technology

Chord Reaper uses multiple machine learning models, each optimized for specific audio analysis tasks.

Beat Detection Models

Madmom

Default

Neural network with high accuracy and speed, best for common time signatures (3/4, 4/4).

Best for: Pop, Rock, Electronic music

Beat-Transformer

Deep learning model with 5-channel audio separation, flexible in time signatures, slower processing speed.

Best for: Complex mixes, layered instrumentation

Chord Recognition Models

Chord-CNN-LSTM

Default

Convolutional and LSTM neural network for chord recognition with 301 chord labels. Excellent balance of accuracy and performance.

Labels: 301 chord types • Best for: General purpose chord recognition

Song Structure Analysis

SongFormer

Transformer-based model for song structure segmentation. Identifies verse, chorus, bridge, intro, outro, and other musical sections.

Best for: Structural analysis and section-level song navigation