# Music Sep Eval

> Evaluates zero-shot language-queried audio source separation on musical instrument classes. The benchmark tests the model's ability to isolate a target instrument from a mixed audio mixture using text labels. Use when the user wants to benchmark on MUSIC, or asks about evaluating this task. Reports SDRi.

- Skill: `qhjqhj00/music-sep-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/music-sep-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/music-sep-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/music-sep-eval

---


# music-sep-eval

> Separate Anything You Describe — Liu et al. (2023) (arXiv:2308.05037, 2023)

## What this evaluates

Evaluates zero-shot language-queried audio source separation on musical instrument classes. The benchmark tests the model's ability to isolate a target instrument from a mixed audio mixture using text labels.

## Datasets

- **MUSIC** — total 5004; splits: test (5004)

## Metrics

- `SDRi` **(primary)** — range: dB
  - Signal-to-distortion ratio improvement, calculated as the difference between the SDR of the separated output and the original mixture.
- `SI-SDR` — range: dB
  - Scale-invariant signal-to-distortion ratio, evaluates separation quality independent of amplitude scaling between prediction and target.

## Input / output format

**Input**: A 10-second audio mixture (SNR 0 dB) containing a target instrument and other instruments, paired with a text label query specifying the target instrument.

**Output**: Separated audio waveform corresponding to the target instrument specified by the text query.

## Scoring recipe

```python
def score(pred, gold, mixture):
  sdr_out = compute_sdr(pred, gold)
  sdr_mix = compute_sdr(mixture, gold)
  sdri = sdr_out - sdr_mix
  si_sdr = compute_si_sdr(pred, gold)
  return sdri, si_sdr
```

## Common pitfalls

- Original video recordings are segmented into non-overlapping 10-second clips before mixing.
- Mixtures are created by selecting one segment from each of the other instrument classes, not just random pairs.

## Evidence (verbatim from paper)

> The MUSIC dataset is a collection of 536 video recordings of people playing a musical instrument from 11 instrument classes. ... resulting in a total of 5004 evaluation pairs, which are used to evaluate the zero-shot performance of our model on musical instrument separation. ... We utilize signal-to-distortion ratio improvement (SDRi) and scale-invariant SDR (SI-SDR) to evaluate the performance of sound separation systems.

## Citation

```bibtex
@misc{liu2023separate,
  title={Separate Anything You Describe},
  author={Liu et al. (2023)},
  year={2023},
  note={arXiv:2308.05037}
}
```

- arXiv: 2308.05037