Btc Hcqt
An HCQT (Harmonic-CQT) front-end variant of BTC (Park et al., ISMIR 2019) for automatic chord recognition / chord detection — turning audio into a time-stamped chord progression.
Model source
- Kind
- Other
- File size
- 11.7 MB
- Version
- vBeatlesft
- Updated
- Oct 25, 2026
- Source
- Hugging Face
Source description
BTC-HCQT — an HCQT variant of BTC for chord recognition
An HCQT (Harmonic-CQT) front-end variant of BTC (Park et al., ISMIR 2019) for automatic chord recognition / chord detection — turning audio into a time-stamped chord progression.
Honest result: on a reproducible mir_eval benchmark over held-out public data (GuitarSet, Schubert Winterreise), this model ties baseline BTC — it does not clearly beat it. Shared as a reproducible benchmark, an honest negative result, and an extensible HCQT base — not a new state of the art.
Benchmark (held-out, public data, duration-weighted %)
| Model | GuitarSet root / 7ths | Schubert root / 7ths / mirex |
|---|---|---|
| baseline BTC | 80.9 / 64.6 | 73.1 / 55.3 / 64.1 |
| this model (BTC+HCQT, Beatles-FT) | 80.5 / 63.0 | 73.8 / 55.6 / 65.3 |
A dead heat — BTC noses ahead on guitar, this model noses ahead on classical, every gap within the 95% confidence intervals.
Usage, code & full benchmark
Code, weights, the reproducible mir_eval harness, and a guide to extend the HCQT base to melody/bass/transcription: https://github.com/marcusfkelley/btc-hcqt
Limitations & honest notes
It ties BTC; for accuracy alone, baseline BTC is the simpler choice. An early in-house metric suggested big 7th gains — that was a recall artifact (over-calling 7ths); on frame-wise mir_eval it disappears, so always evaluate with standard metrics and confidence intervals. For pure note transcription, purpose-built tools (basic-pitch, MT3, Omnizart) will outperform a from-here build — HCQT is a strong base to extend toward melody/bass/transcription, not a finished transcriber.
Source
Built by Selekt — cleared-sample and music-analysis tools for producers and composers; chord recognition powers features like our chord-progression search. Honest writeup: https://selektaudio.com/chord-recognition
Built on BTC (Park et al., ISMIR 2019, MIT). HCQT: Bittner et al., ISMIR 2017.
Sources
1 source
Sources
1 sourceVerified Oct 25
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit library_name: pytorch pipeline_tag: audio-classification tags: [audio, music, chord-recognition, chord-detection, automatic-chord-recognition, music-information-retrieval, mir, hcqt, btc] datasets: [guitarset] metrics: [accuracy] --- # BTC-HCQT — an HCQT variant of BTC for chord recognition An HCQT (Harmonic-CQT) front-end variant of **BTC** (Park et al., ISMIR 2019) for **automatic chord recognition / chord detection** — turning audio into a time-stamped chord progression. **Honest result:** on a reproducible mir_eval benchmark over held-out public data (GuitarSet, Schubert Winterreise), this model **ties baseline BTC** — it does **not** clearly beat it. Shared as a reproducible benchmark, an honest negative result, and an extensible HCQT base — not a new state of the art. ## Benchmark (held-out, public data, duration-weighted %) | Model | GuitarSet root / 7ths | Schubert root / 7ths / mirex | |---|---|---| | baseline BTC | **80.9 / 64.6** | 73.1 / 55.3 / 64.1 | | **this model (BTC+HCQT, Beatles-FT)** | 80.5 / 63.0 | **73.8 / 55.6 / 65.3** | A dead heat — BTC noses ahead on guitar, this model noses ahead on classical, every gap within the 95% confidence intervals. ## Usage, code & full benchmark Code, weights, the reproducible mir_eval harness, and a guide to extend the HCQT base to melody/bass/transcription: **https://github.com/marcusfkelley/btc-hcqt** ## Limitations & honest notes It **ties** BTC; for accuracy alone, baseline BTC is the simpler choice. An early in-house metric suggested big 7th gains — that was a **recall artifact** (over-calling 7ths); on frame-wise mir_eval it disappears, so always evaluate with standard metrics and confidence intervals. For pure note transcription, purpose-built tools (basic-pitch, MT3, Omnizart) will outperform a from-here build — HCQT is a strong base to *extend* toward melody/bass/transcription, not a finished transcriber. ## Source Built by [Selekt](https://selektaudio.com) — cleared-sample and music-analysis tools for producers and composers; chord recognition powers features like our chord-progression search. Honest writeup: https://selektaudio.com/chord-recognition Built on BTC (Park et al., ISMIR 2019, MIT). HCQT: Bittner et al., ISMIR 2017.
Source context: 0 downloads · 1 likes · Pipeline audio-classification · Library pytorch · Repo Marcusfkelley/btc-hcqt
