---
license: mit
tags:
- audio
- sound-effects
- retrieval
- contrastive-learning
pipeline_tag: feature-extraction
---

# Doppelganger — trained heads and fine-tuned encoders

Models for the Doppelganger benchmark (matching a synthetic sound effect to the real recording it was
generated from). Paper: https://arxiv.org/abs/2607.04337 · Code: https://github.com/elliottash/doppelganger · dataset:
https://huggingface.co/datasets/elliottash/doppelganger

## Contents

- `heads/` — trained MLP heads (`*.head.pt`), the paper's models. Each is a compact head
  (`d → 512 → 256`, batch-norm, ReLU, ℓ2-normalized) on top of a frozen encoder. Naming:
  `<encoder>_ucs_paired[_<variant>]_<objective>.head.pt`, with `objective ∈ {instance, class}` and
  per-fold leave-classes-out (`kf0..kf4`), data-efficiency (`sub250..sub4000`), and cross-generator
  (`aldm`) variants.
  - **instance** head — the generalizing one (learns the synthetic→real instance correspondence).
  - **class** head — class-supervised control (collapses below frozen on unseen events).
- `ckpts/` — fine-tuned encoder checkpoints (BEATs, M2D) used in the six-encoder robustness study.

## Use

```python
import torch
head = torch.load("heads/clap_general_ucs_paired_instance.head.pt", map_location="cpu")
# apply to frozen encoder embeddings (see src/apply_head.py in the code repo)
```

The heads consume frozen-encoder embeddings; the frozen backbones (CLAP, PANNs, AST, AudioMAE) come
from their original releases, and the BEATs/M2D fine-tunes are in `ckpts/`.

## License

MIT (heads and fine-tunes). Frozen backbones follow their original licenses.