# Voice Accompaniment Separation Eval

> Evaluates a model's ability to separate vocal and accompaniment tracks from mixed music audio. It probes long-term dependency modeling and pattern repetition exploitation in audio source separation. Use when the user wants to benchmark on DSD100, MedleyDB, CCMixer, or asks about evaluating this task. Reports SDR.

- Skill: `qhjqhj00/voice-accompaniment-separation-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/voice-accompaniment-separation-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/voice-accompaniment-separation-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/voice-accompaniment-separation-eval

---


# voice-accompaniment-separation-eval

> Voice and accompaniment separation in music using self-attention convolutional neural network — Liu et al. (2020) (arXiv:2003.08954, 2020)

## What this evaluates

Evaluates a model's ability to separate vocal and accompaniment tracks from mixed music audio. It probes long-term dependency modeling and pattern repetition exploitation in audio source separation.

## Datasets

- **DSD100** — total ?; splits: test (25)
- **MedleyDB** — total ?; splits: val (-1), test (-1)
- **CCMixer** — total ?; splits: val (-1), test (-1)

## Metrics

- `SDR` **(primary)** — range: dB
  - Track-wise normalised Signal-to-Distortion Ratio in decibels (dB). Average results across the test set are reported.
- `SIR` — range: dB
  - Track-wise normalised Signal-to-Interference Ratio in decibels (dB). Average results across the test set are reported.
- `SAR` — range: dB
  - Track-wise normalised Signal-to-Artifact Ratio in decibels (dB). Average results across the test set are reported.

## Input / output format

**Input**: Mixed music audio tracks downsampled to 16 kHz.

**Output**: Separated vocal and accompaniment audio tracks.

## Scoring recipe

```python
# For each track in the test set:
# 1. Compute track-wise normalised SDR, SIR, and SAR between predicted and ground truth sources.
# 2. Average the metric values across all tracks in the test set.
# 3. Report mean SDR, SIR, SAR for both voice and accompaniment.
```

## Common pitfalls

- The test set is a custom combination of 1/3 MedleyDB, 1/3 CCMixer, and 25 DSD100 test tracks, not the standard DSD100 test set.
- All audio must be downsampled to 16 kHz before evaluation.
- Baselines like MMDenseLSTM may yield different results if trained with different structures or recipes, so direct comparison requires identical training setups.

## Evidence (verbatim from paper)

> We calculate the track-wise (normalised) SDR, SIR, and SAR as metrics. The average results on the test set are reported in Table I.

## Citation

```bibtex
@misc{liu2020voice,
  title={Voice and accompaniment separation in music using self-attention convolutional neural network},
  author={Liu et al. (2020)},
  year={2020},
  note={arXiv:2003.08954}
}
```

- arXiv: 2003.08954