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Model

Yamnetp Ahead Ds

Name: Yamnetp Ahead Ds
Author: hzhongresearch

by hzhongresearch hf-model--hzhongresearch--yamnetp_ahead_ds

Free2AITools Nexus Index

41.1 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 34

R: Recency 89

Q: Quality 50

Tech Context

Vital Performance

2.8K DL / 30D

0.0%

Source →

Audited 41.1 FNI Score

Tiny - Params

- Context

2.8K Downloads

Restricted CC License

Model Information Summary
Entity Passport
Registry ID	hf-model--hzhongresearch--yamnetp_ahead_ds
License	CC-BY-SA-4.0
Provider	huggingface

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Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__hzhongresearch__yamnetp_ahead_ds,
  author = {hzhongresearch},
  title = {Yamnetp Ahead Ds Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/hzhongresearch/yamnetp_ahead_ds}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

hzhongresearch. (2026). Yamnetp Ahead Ds [Model]. Free2AITools. https://huggingface.co/hzhongresearch/yamnetp_ahead_ds

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

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huggingface-cli download hzhongresearch/yamnetp_ahead_ds

⚖️ Free2AITools Nexus Index V2.0

Methodology Index Protocol

Semantic (S) 50

Authority (A) 0

Popularity (P) 34

Recency (R) 89

Quality (Q) 50

💬 Index Insight

FNI V2.0 for Yamnetp Ahead Ds: Semantic (S:50), Authority (A:0), Popularity (P:34), Recency (R:89), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

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Technical Deep Dive

OpenYAMNet/YAMNet+

This repository contains the models for OpenYAMNet/YAMNet+, introduced in the paper A dataset and model for auditory scene recognition for hearing devices: AHEAD-DS and OpenYAMNet.

OpenYAMNet is a sound recognition model designed for deployment on edge devices like smartphones connected to hearing devices (e.g., hearing aids and wireless earphones). It serves as a baseline model for sound-based scene recognition.

Model Performance

OpenYAMNet achieved the following results on the testing set of the AHEAD-DS dataset:

Mean Average Precision (mAP): 0.86
Accuracy: 0.93

The model is optimized for real-time use, with approximately 50ms of latency for loading and a processing time of ~30ms per 1 second of audio on a 2018 Google Pixel 3.

Resources

Supported Scene Classes

Classes
cocktail_party
interfering_speakers
in_traffic
in_vehicle
music
quiet_indoors
reverberant_environment
wind_turbulence
speech_in_traffic
speech_in_vehicle
speech_in_music
speech_in_quiet_indoors
speech_in_reverberant_environment
speech_in_wind_turbulence

Licence

Licenced under CC BY-SA 4.0. See LICENCE.txt. Licence for original YAMNet weights.

Citation

bibtex

@misc{zhong2026datasetmodelauditoryscene,
      title={A dataset and model for auditory scene recognition for hearing devices: AHEAD-DS and OpenYAMNet}, 
      author={Henry Zhong and Jörg M. Buchholz and Julian Maclaren and Simon Carlile and Richard Lyon},
      year={2026},
      eprint={2508.10360},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2508.10360}, 
}

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• FNI scores are relative rankings and may change as new models are added.
⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

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2.8KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

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AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-model--hzhongresearch--yamnetp_ahead_ds
slug: hzhongresearch--yamnetp_ahead_ds
source: huggingface
author: hzhongresearch
license: CC-BY-SA-4.0
tags: keras, tflite, tf-keras, audio, audio-classification, en, arxiv:2508.10360, license:cc-by-sa-4.0, region:us

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag: audio-classification

📊 Engagement & Metrics

downloads: 2,834
stars: 0
forks: 0

Data indexed from public sources. Updated daily.