Evaluates zero-shot language-queried audio source separation using natural language captions rather than fixed labels. The benchmark tests the model's ability to separate a target sound described by human-annotated captions from a mixed audio mixture. Use when the user wants to benchmark on AudioCaps, or asks about evaluating this task. Reports SDRi.
Scanned 9/11/2026
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---
name: audiocaps-sep-eval
description: Evaluates zero-shot language-queried audio source separation using natural language captions rather than fixed labels. The benchmark tests the model's ability to separate a target sound described by human-annotated captions from a mixed audio mixture. Use when the user wants to benchmark on AudioCaps, or asks about evaluating this task. Reports SDRi.
metadata:
skill_kind: dataset_eval
source_arxiv: 2308.05037
bibtex_key: liu2023separate
confidence: high
---
# audiocaps-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 using natural language captions rather than fixed labels. The benchmark tests the model's ability to separate a target sound described by human-annotated captions from a mixed audio mixture.
## Datasets
- **AudioCaps** — total 4785; splits: test (4785)
## 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 sound and background source, paired with one of five human-annotated natural language captions for the target.
**Output**: Separated audio waveform corresponding to the target sound described by the caption.
## 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
- Each test audio is mixed with five randomly chosen background sources, creating 5 variations per original clip.
- Background sources are selected such that their sound event tags do not coincide with the target source's tags.
## Evidence (verbatim from paper)
> Our downloaded test set of the AudioCaps dataset includes 957 audio clips, each annotated with five captions. ... Consequently, 4785 test mixtures are created. ... 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
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