Fixing music discovery through structural math and quantified emotion.
Current streaming algorithms rely on popularity and vague genre tags, causing emotional whiplash when playlists transition from stable to aggressive moods.
The Chaos Meter is a predictive algorithm that calculates an artist's Mood Volatility. It provides users with an instant, auditable score for how emotionally stable or erratic an artist's output is. This is the application of the LLL structural analysis to the external music ecosystem.
GOAL: Guarantee the emotional consistency of the next track, ensuring the integrity of the user's listening environment.
The traditional discovery process relies on subjective genre labels and unreliable user tagging. Tu-Nerr bypasses this system to build its discovery engine based purely on verifiable mathematical structure and texture.
We analyze the underlying patterns in rhythm, harmony, and timbre by leveraging validated, industry-standard data: Structural Music Descriptors.
METHOD: By calculating the structural logic of the current track, we guarantee the vibe of the next song matches that same architectural footprint.