qhjqhj00 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
# 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
@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