---
license: apache-2.0
language:
- en
---
---
language: en
tags:
  - audio
  - audio-classification
  - respiratory-sounds
  - healthcare
  - medical
  - hear
  - vit
  - lora
  - pytorch
license: apache-2.0
datasets:
  - SPRSound
metrics:
  - accuracy
  - f1
  - roc_auc
base_model: google/hear-pytorch
pipeline_tag: audio-classification
---

# HeAR-SPRSound: Respiratory Sound Abnormality Classifier

## Model Summary

A fine-tuned respiratory sound classifier built on top of **Google's HeAR** (Health Acoustic Representations) foundation model. The model performs **binary classification** — distinguishing **normal** from **abnormal** respiratory sounds — and is trained on the **SPRSound** dataset spanning BioCAS challenge years 2022–2025.

The architecture combines the HeAR ViT backbone (fine-tuned with LoRA) with a **Gated Attention Pooling** layer that intelligently aggregates variable-length audio sequences chunk by chunk, followed by a two-layer MLP classifier.

---

## Architecture

```
Audio Input (16 kHz WAV)
       ↓
HeAR Preprocessing (2-second chunks, log-mel spectrograms [1 × 192 × 128])
       ↓
HeAR ViT Encoder (google/hear-pytorch)
  └─ LoRA adapters on Q & V projections in last 6 transformer blocks
       ↓
Per-chunk CLS Embeddings [B × T × 512]
       ↓
Gated Attention Pooling (length-masked softmax attention over chunks)
       ↓
Pooled Representation [B × 512]
       ↓
MLP Classifier (512 → 256 → 2, GELU, Dropout 0.4)
       ↓
Normal / Abnormal
```

**Key components:**
- **Backbone**: `google/hear-pytorch` (frozen except LoRA layers + LayerNorms)
- **LoRA**: rank=16, alpha=16, dropout=0.3, applied to Q+V projections in last 6 blocks
- **Pooling**: Gated Attention Pool (dual-path tanh × sigmoid gating, hidden dim 512)
- **Loss**: Focal Loss (γ=2.0) with class-balanced sample weighting
- **Inference**: Per-class threshold optimization (one-vs-rest F1 on validation set)

---

## Training Details

| Hyperparameter | Value |
|---|---|
| Base model | `google/hear-pytorch` |
| Input sample rate | 16,000 Hz |
| Chunk size | 2 seconds (32,000 samples) |
| Max audio duration | 10 seconds (up to 5 chunks) |
| Optimizer | AdamW |
| Learning rate | 5e-5 |
| Weight decay | 0.2 |
| Warmup epochs | 10 |
| Max epochs | 100 |
| Batch size | 96 |
| Early stopping patience | 20 epochs |

---

## Dataset

**SPRSound** — multi-year BioCAS challenge respiratory auscultation dataset.

| Year | Split |
|---|---|
| BioCAS 2022 | Train + Inter/Intra test |
| BioCAS 2023 | Test |
| BioCAS 2024 | Test |
| BioCAS 2025 | Test |

All data was **re-split at the patient level** (70% train / 15% val / 15% test) to prevent data leakage. No patient appears in more than one split. Labels were consolidated to a binary scheme:

- **normal**: all event annotations are "Normal"
- **abnormal**: any non-normal respiratory event present (wheeze, crackle, rhonchus, etc.)

Class imbalance was addressed through `WeightedRandomSampler` and Focal Loss.

---

## Data Augmentation

A custom `PhoneLikeAugment` pipeline was applied during training (p=0.5) to simulate real-world acoustic variability:

- Random gain (−18 to +8 dB)
- Phone band-limiting (HP: 120–200 Hz, LP: 4–8 kHz)
- Fast echo / room simulation (10–80 ms delay taps)
- Colored noise addition (SNR 3–25 dB)
- Soft AGC / tanh compression
- Random time shift (±80 ms)
- Rare clipping (p=0.15)

---

## Usage

```python
import torch
import torchaudio
from transformers import AutoModel
# Load model
model = AdaptiveRespiratoryModel(
    num_classes=2,
    dropout=0.4,
    use_lora=True,
    lora_r=16,
    lora_alpha=16,
    lora_dropout=0.3,
    lora_last_n_blocks=6
)
checkpoint = torch.load("best_model.pth", map_location="cpu", weights_only=False)
model.load_state_dict(checkpoint["model"], strict=False)
model.eval()

# Audio must be 16 kHz, processed through HeAR's preprocess_audio
# into chunks of shape [T, 1, 192, 128]
```

> ⚠️ Requires `google/hear-pytorch` and the [HEAR](https://github.com/Google-Health/hear) library for audio preprocessing.

---

## Limitations & Intended Use

- **Intended use**: Research and prototyping in respiratory sound analysis. **Not validated for clinical use.**
- The model was trained on auscultation recordings from SPRSound; performance may degrade on recordings from different stethoscope types, microphones, or patient populations.
- Binary classification only — does not distinguish between specific pathology types (e.g., wheeze vs. crackle).
- Threshold calibration was performed on the validation set; recalibration is recommended when deploying to new domains.

---

## Citation

If you use this model, please cite the SPRSound dataset and the HeAR foundation model:

```bibtex
@misc{sprsound,
  title   = {SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database},
  year    = {2022},
  note    = {BioCAS 2022–2025 challenge dataset}
}

@misc{hear2024,
  title   = {HeAR: Health Acoustic Representations},
  author  = {Google Health},
  year    = {2024},
  url     = {https://github.com/Google-Health/hear}
}
```

---

## License

This model is released under the **Apache 2.0** license. The HeAR backbone model is subject to Google's original license terms. SPRSound data is subject to its own terms — please refer to the dataset authors.