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Sound Event Detection

Pretrained models for Sound Event Detection (SED) used in MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing".

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Model source

Kind
Other
File size
987 MB
Version
vBest
Updated
Nov 5, 2026
Source
Hugging Face

Source description

Sound Event Detection — Pretrained Models

Pretrained models for Sound Event Detection (SED) used in MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing".

Models

1. YAMNet (Pretrained Baseline)

  • Source: google/yamnet (TensorFlow) / PyTorch reimplementation
  • Classes: 521 AudioSet classes
  • Usage: Loaded directly from HuggingFace — no checkpoint in this repo

2. AST (Pretrained Baseline)

3. Fine-tuned AST (sed_ast_snr_ctl_v2_16k)

  • Base model: MIT/ast-finetuned-audioset-10-10-0.4593
  • Fine-tuned on: On-the-fly synthesized binaural audio mixtures (SNR-controlled, 16kHz)
  • Classes: 20 target sound classes
  • Training: AdamW, OneCycleLR with group-wise learning rates (backbone 1e-5, head 1e-3), 80 epochs
  • Checkpoint: sed_ast_snr_ctl_v2_16k/checkpoints/best.pt

File Structure

.
├── README.md
└── sed_ast_snr_ctl_v2_16k/
    ├── config.json          # Training configuration
    └── checkpoints/
        └── best.pt          # Fine-tuned model weights (~2GB)

Usage

# Fine-tuned AST
from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download(
    repo_id="ooshyun/sound_event_detection",
    filename="sed_ast_snr_ctl_v2_16k/checkpoints/best.pt",
)

config_path = hf_hub_download(
    repo_id="ooshyun/sound_event_detection",
    filename="sed_ast_snr_ctl_v2_16k/config.json",
)

For training and evaluation code, see ooshyun/sound_event_detection.

Citation

If you use these models, please cite:

MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing"

Sources

1 source
Hugging FacePrimaryVersion db06e38d143c36e56c28fbf5c999ee0ac6804a3a
huggingface.co/ooshyun/sound_event_detection/tree/db06e38d143c36e56c28fbf5c999ee0ac6804a3a

Verified Nov 5

Model artifacts

1 artifact

sed_ast_snr_ctl_v2_16k/checkpoints/best.pt

pt · 987 MB · SHA-256 1a640a4e87be…815d · Hugging Face

Download file

Source excerpts

2 excerpts

--- license: mit tags: - audio - sound-event-detection - audio-spectrogram-transformer - yamnet datasets: - audioset language: - en pipeline_tag: audio-classification --- # Sound Event Detection — Pretrained Models Pretrained models for Sound Event Detection (SED) used in **MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing"**. ## Models ### 1. YAMNet (Pretrained Baseline) - **Source**: [google/yamnet](https://huggingface.co/google/yamnet) (TensorFlow) / PyTorch reimplementation - **Classes**: 521 AudioSet classes - **Usage**: Loaded directly from HuggingFace — no checkpoint in this repo ### 2. AST (Pretrained Baseline) - **Source**: [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) - **Architecture**: Audio Spectrogram Transformer - **Classes**: 527 AudioSet classes - **Usage**: Loaded directly from HuggingFace — no checkpoint in this repo ### 3. Fine-tuned AST (`sed_ast_snr_ctl_v2_16k`) - **Base model**: `MIT/ast-finetuned-audioset-10-10-0.4593` - **Fine-tuned on**: On-the-fly synthesized binaural audio mixtures (SNR-controlled, 16kHz) - **Classes**: 20 target sound classes - **Training**: AdamW, OneCycleLR with group-wise learning rates (backbone 1e-5, head 1e-3), 80 epochs - **Checkpoint**: `sed_ast_snr_ctl_v2_16k/checkpoints/best.pt` ## File Structure ``` . ├── README.md └── sed_ast_snr_ctl_v2_16k/ ├── config.json # Training configuration └── checkpoints/ └── best.pt # Fine-tuned model weights (~2GB) ``` ## Usage ```python # Fine-tuned AST from huggingface_hub import hf_hub_download checkpoint_path = hf_hub_download( repo_id="ooshyun/sound_event_detection", filename="sed_ast_snr_ctl_v2_16k/checkpoints/best.pt", ) config_path = hf_hub_download( repo_id="ooshyun/sound_event_detection", filename="sed_ast_snr_ctl_v2_16k/config.json", ) ``` For training and evaluation code, see [ooshyun/sound_event_detection](https://github.com/ooshyun/sound_event_detection). ## Citation If you use these models, please cite: ``` MobiSys 2026 #198 "Fine-grained Soundscape Control for Augmented Hearing" ```

Source context: 0 downloads · 0 likes · Pipeline audio-classification · Repo ooshyun/sound_event_detection