FreqBlog Music Metadata

Get audio features for ONE track — BPM, musical key (name + Camelot + Open Key), energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness, loudness, mood, mood_vector, genre, time signature, duration and more. This is the drop-in replacement for Spotify's dep

Get audio features for MANY tracks in one call (up to 50 processed) — ideal for analysing a whole playlist at once. Identify each item by name (`track`/`artist`), by `isrc` (matched exactly first — best for CJK / K-pop / niche tracks whose fuzzy name-match misses), or both (ISRC first, n

Full-text search the catalog by any mix of track / artist / album tokens. Use this to resolve a fuzzy, partial, or misspelled name into concrete tracks BEFORE calling get_audio_features. Returns lightweight stubs (itunes_track_id, track_name, artist_name, album, etc.) ranked by rele

Find catalog tracks near a target tempo. Returns tracks whose BPM is within +/-`tolerance` of `bpm`, ordered by closeness then popularity — useful for DJ set planning, workout playlists, or tempo-matching. Each returned track carries full audio features. To also constrain by musical key,

Find catalog tracks in a given musical key — for harmonic mixing and key-locked playlists. `key` accepts Camelot ("8A"), Open Key ("1m"), or a key name ("A-Minor", "F#-Major"). Returns tracks ordered by popularity, each with full audio features. To discover which keys mix well with a giv

Given a Camelot key (e.g. "8A", "12B"), return the harmonically compatible keys for DJ mixing — the same key, the relative major/minor, and the adjacent +/-1 keys on the Camelot wheel. With `extended=true` also returns the +7/-7 energy-boost / energy-drop keys. Pure music theory — no cat

Score how well one catalog track mixes into another (0-100) — the pairwise DJ transition score no raw key/BPM API gives you. Combines Camelot-wheel key compatibility, octave-aware BPM proximity (half/double-time counts as a match), and energy smoothness. Returns the overall `score`, per

Given a seed track, return the top-N catalog tracks to play NEXT, ranked by transition score. Each suggestion carries the same `score`, per-component scores and human `reason` as score_transition (e.g. "11B->11B same key, 118->117 BPM (-0.29), energy +0.12"), plus its `genre` and `genre_

Order a crate of 2-100 catalog tracks into a beat-matched DJ set that follows an energy arc, keeping each consecutive transition harmonically and tempo-smooth. `arc` is one of peak_time (default — builds to a peak then eases), warmup, cooldown, or flat. Returns the `arc`, `count`, an ov

Recommended tracks for one or more seed tracks — the drop-in for Spotify's removed GET /v1/recommendations. Blends up to 5 catalog seed tracks into a single point in audio-feature space and returns the nearest catalogue tracks, RE-RANKED by genre affinity (so a feature-close cross-genre

Artists related to a seed artist — the drop-in for Spotify's removed GET /v1/artists/{id}/related-artists. No artist graph exists, so we derive one: build the seed artist's track-vector centroid, take its nearest catalogue tracks, aggregate by artist (each scored on its top-3 track simil

Get a compact, HONESTLY-LABELLED tag list for a track — energy / danceability / valence / acousticness / instrumentalness, plus a mood tag and a broad genre tag. It is a tag-shaped projection of the same open-data analysis get_audio_features returns (no audio upload, no extra compute), s