Mtass Sdr Eval

---
name: mtass-sdr-eval
description: Evaluates a model's ability to simultaneously separate speech, music, and background noise from monaural audio mixtures. It measures separation fidelity using signal-to-distortion ratio and its improvement over baselines across all three source tracks. Use when the user wants to benchmark on Unspecified (speech, music, noise tracks), or asks about evaluating this task. Reports SDRi (dB).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2107.06467
  bibtex_key: zhang2021multi
  confidence: high
---

# mtass-sdr-eval

> Multi-Task Audio Source Separation — Lu Zhang et al. (2021) (arXiv:2107.06467, 2021)

## What this evaluates

Evaluates a model's ability to simultaneously separate speech, music, and background noise from monaural audio mixtures. It measures separation fidelity using signal-to-distortion ratio and its improvement over baselines across all three source tracks.

## Datasets

- **Unspecified (speech, music, noise tracks)** — total ?; splits: test (-1)

## Metrics

- `SDR (dB)` — range: dB
  - Signal-to-Distortion Ratio: $10\log_{10}(\|s_{\text{target}}\|^2 / \|e_{\text{interf}}+e_{\text{noise}}+e_{\text{artif}}\|^2)$, where $s_{\text{target}}$ is the target source and error terms represent interference, noise, and artifacts.
- `SDRi (dB)` **(primary)** — range: dB
  - SDR improvement, calculated as the difference between the SDR of the separated signal and the SDR of the original mixture. Reported as the headline metric across all comparison tables.

## Input / output format

**Input**: Monaural audio mixture containing overlapping speech, music, and background noise.

**Output**: Three separated audio waveforms corresponding to the speech, music, and noise tracks.

## Scoring recipe

```python
def compute_sdri(separated, mixture, target):
    sdr_sep = 10 * log10(norm(target)**2 / norm(separated - target)**2)
    sdr_mix = 10 * log10(norm(mixture)**2 / norm(mixture - target)**2)
    return sdr_sep - sdr_mix

# Compute SDRi for each track (speech, music, noise)
# Report mean SDRi across tracks as the primary evaluation score.
```

## Common pitfalls

- Confusing absolute SDR with SDRi (improvement over mixture), leading to incorrect baseline comparisons.
- Reporting results for only one source track instead of providing balanced metrics across speech, music, and noise.
- Ignoring spectral phase when evaluating frequency-domain baselines, which unfairly penalizes complex-domain models that explicitly model phase.

## Evidence (verbatim from paper)

> Separation performance is measured by the signal-to-distortion ratio (SDR) [[38]], which is defined as: SDR := 10log10(||s_target||^2 / ||e_interf + e_mise + e_artif||^2) where e_interf, e_noise, and e_artif are, respectively, the interferences, noise, and artifacts error terms. Table 3: SDR improvement (SDRi) for different training targets of the proposed separator obtained on the speech, music, and noise signal tracks.

## Citation

```bibtex
@misc{zhang2021multi,
  title={Multi-Task Audio Source Separation},
  author={Lu Zhang et al. (2021)},
  year={2021},
  note={arXiv:2107.06467}
}
```

- arXiv: 2107.06467