---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: audio-classification
tags:
- audio
- telephony
- answering-machine-detection
- voicemail-detection
- yamnet
- gru
- 8khz
- vicidial
- asterisk
---

# AMD — Answering Machine Detection for telephony (YAMNet + GRU)

Real-time answering-machine detection for 8 kHz US-English telephony
(VICIdial / Asterisk outbound campaigns). A pure-PyTorch YAMNet backbone
(fine-tuned, top-2 blocks) feeds a bidirectional GRU head; output is
`p_machine ∈ [0,1]`. **~16 ms inference per 4 s of audio on a single CPU
thread** — fast enough to decide inside the first ring seconds.

Two checkpoints:

| checkpoint | training pool | how it was measured |
|---|---|---|
| **`v5b_full` (root)** | 50,876 clips — the full max-data pool (incl. real carrier voicemail, IVR, network intercepts, robocalls, call-center + conversational telephone speech) | validation (4,949 held-out clips) |
| `benchmarked_v4a/` | 31,947 clips (frozen-eval-disjoint) | two frozen external test sets |

## Results

`v5b_full` (validation, threshold 0.5): **97.7% accuracy**, machine recall
**97.0%** under a ≤3% false-machine-on-humans cap, fmh 1.56%. On held-out
clips of the hardest class — real carrier voicemail greetings — error is
**13%**, versus 91% for a naive-composition baseline.

`benchmarked_v4a` (frozen external sets, never trained on, one pass): 79.8%
accuracy / balanced-acc .845 / AUC .935 on a 796-clip external set;
92.5% / .928 / .979 on a 1,429-clip balanced set; machine recall at the
FTC-style ≤3% abandonment cap: 0.56–0.91.

## Usage

```bash
pip install torch numpy soundfile scipy
python code/predict_example.py your_call.wav
```

```python
import sys; sys.path.insert(0, "code")
from predict_example import load, predict
yamnet, head = load()
p_machine = predict(yamnet, head, "call.wav")   # 0..1
```

Audio: any wav (resampled to 16 kHz mono internally; trained domain is 8 kHz
G.711 telephony, first ≤10 s of the call).

## Intended use & limitations

- **Research / evaluation only (CC BY-NC 4.0).** Trained on a mixed-license
  research pool; not cleared for commercial deployment.
- US-English telephony. Non-English and non-telephone audio are out of domain.
- Decision thresholds must be **calibrated on your own audio** — false-machine
  rates measured on any fixed test set do not transfer exactly to live traffic.
- `v5b_full` has no fully-external benchmark (its pool absorbed the project's
  test sources — by design, for maximum coverage); `benchmarked_v4a` is the
  checkpoint with honest external numbers. Known weak spots: robocall recall
  (v5 val slice: 26% miss), heavily accented/studio-recorded humans.

## Architecture

- Backbone: YAMNet (AudioSet), pure-PyTorch port, custom `torch.stft` mel
  frontend — no TensorFlow; top-2 blocks fine-tuned (BN frozen), bf16.
- Head: bidirectional GRU over 1024-d patch embeddings (0.96 s window /
  0.48 s hop), ~350k params, label smoothing 0.1.
- Files: `*.yamnet.pth` (backbone, 15 MB) + `*.head.pt` (head + config, 1 MB)
  + `*.json` (export manifest with round-trip verification).