Musdb Eval

---
name: musdb-eval
description: Evaluates a model's ability to isolate individual musical stems (vocals, drums, bass, other) from mixed audio recordings, testing long-range context modeling and cross-domain attention capabilities in source separation. Use when the user wants to benchmark on MUSDB, or asks about evaluating this task. Reports SDR.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2211.08553
  bibtex_key: [REDACTED]
  confidence: medium
---

# musdb-eval

> Hybrid Transformers for Music Source Separation — Rouard et al. (2022) (arXiv:2211.08553, 2022)

## What this evaluates

Evaluates a model's ability to isolate individual musical stems (vocals, drums, bass, other) from mixed audio recordings, testing long-range context modeling and cross-domain attention capabilities in source separation.

## Datasets

- **MUSDB** — total ?; splits: test (-1)

## Metrics

- `SDR` **(primary)** — range: dB
  - Signal-to-Distortion Ratio measures the ratio of the target source energy to the sum of interference, artifact, and noise energy. Higher values indicate better separation quality.

## Input / output format

**Input**: Mixed audio waveform containing multiple overlapping musical sources.

**Output**: Four separated audio stems corresponding to vocals, drums, bass, and other.

## Scoring recipe

```python
sdr_scores = []
for source in ['vocals', 'drums', 'bass', 'other']:
    pred = model.predict(mixed_audio)[:, source]
    true = ground_truth[:, source]
    sdr = compute_sdr(pred, true)
    sdr_scores.append(sdr)
return mean(sdr_scores)
```

## Common pitfalls

- SDR values are highly sensitive to phase alignment and amplitude normalization between predicted and ground truth stems; inconsistent preprocessing leads to non-comparable scores.
- Different source separation papers sometimes report SDR on different subsets of MUSDB or with different reference signal alignments, making direct benchmark comparisons difficult.

## Evidence (verbatim from paper)

> reach state-of-the-art 9.20 dB SDR on MUSDB.

## Citation

```bibtex
@misc{rouard2022hybrid,
  title={Hybrid Transformers for Music Source Separation},
  author={Rouard et al. (2022)},
  year={2022},
  note={arXiv:2211.08553}
}
```

- arXiv: 2211.08553