Cyanite.ai

Analyzes audio files and automatically assigns genre tags based on acoustic characteristics and learned patterns. Processes the full audio to determine primary and secondary genres without manual input.

Detects emotional characteristics and mood attributes from audio analysis, assigning descriptors like energetic, melancholic, uplifting, or dark. Enables mood-based playlist creation and discovery.

Automatically measures the beats per minute (BPM) and tempo characteristics of audio tracks. Provides precise tempo data for DJ mixing, workout playlists, and synchronization purposes.

Detects and lists the instruments, vocals, and sound elements present in a track. Identifies whether vocals are present, what instruments are used, and their prominence in the mix.

Analyzes harmonic content to determine the musical key of a track. Provides key information essential for DJ mixing, music theory analysis, and harmonic compatibility matching.

Processes multiple audio files simultaneously or sequentially, applying all tagging and analysis capabilities across an entire music catalog. Enables rapid metadata generation for large libraries.

Provides programmatic API access to audio analysis capabilities, enabling seamless integration into music platforms, DSPs, and custom workflows. Returns structured metadata that can be directly stored in databases.

Transforms raw audio metadata into searchable, filterable catalog structures. Enables users to query their music library by any combination of tags (genre, mood, BPM, instruments, key).

Provides free access to core tagging capabilities with limited monthly quota, allowing users to test the system before committing to paid plans. Enables risk-free evaluation of accuracy and fit.

Provides interface and tools for human review and correction of AI-generated tags. Enables quality control for mission-critical applications where accuracy is essential.