UrbanSound_EventDetection_Wav2Vec2

UrbanSound_EventDetection_Wav2Vec2

👂 Overview

The UrbanSound_EventDetection_Wav2Vec2 is a highly efficient model based on the pre-trained Wav2Vec2 architecture, fine-tuned specifically for classifying momentary and continuous sound events within urban environments. It processes raw audio waveforms to identify one of eight high-priority urban sound classes, focusing on high-impact and potentially anomalous events.

🧠 Model Architecture

This model utilizes the standard Wav2Vec2 pipeline, which operates directly on raw audio data without the need for manual feature extraction (like MFCCs).

• Base Model: facebook/wav2vec2-base
• Feature Extractor: A stack of 1D convolutional layers extracts local features from the raw waveform.
• Transformer Encoder: 12 layers of Transformer blocks capture long-range dependencies and global context within the audio clip.
• Classification Head: A task-specific linear layer is placed on top of the contextualized representations to predict one of the 8 event labels.
• Target Classes: Car_Horn, Children_Playing, Dog_Barking, Machinery_Hum, Siren_Emergency, Train_Whistle, Tire_Screech, and Glass_Shattering.

🎯 Intended Use

This model is intended for smart city, safety, and acoustic monitoring systems:

1. Acoustic Surveillance: Real-time detection of emergency sounds (Siren, Glass Shattering, Tire Screech) for public safety alerting.
2. Noise Pollution Monitoring: Quantifying the occurrence and frequency of specific noise sources (Car Horn, Machinery Hum) in different city zones.
3. Urban Planning: Analyzing soundscape composition to inform policy on zoning and noise mitigation strategies.

⚠️ Limitations

1. Event Overlap: The current setup is trained for single-label classification. If multiple sounds occur simultaneously (e.g., Siren + Dog Barking), the model will only output the single most probable event, potentially ignoring others.
2. Domain Shift: The model's performance may degrade if deployed in environments with significantly different background noise profiles (e.g., highly quiet suburbs vs. extremely loud Asian markets).
3. Localization: This model performs event detection but does not inherently provide sound localization (Direction-of-Arrival or DOA), which would require specialized input features (like ambisonic audio) and a different model head.

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config.json

{
  "_name_or_path": "roberta-base",
  "architectures": [
    "RobertaForSequenceClassification"
  ],
  "hidden_size": 768,
  "model_type": "roberta",
  "num_hidden_layers": 12,
  "vocab_size": 50265,
  "id2label": {
    "0": "Symptom_Reporting",
    "1": "Advice_Seeking",
    "2": "Medication_Query",
    "3": "Appointment_Scheduling",
    "4": "Billing_Query",
    "5": "Causal_Query",
    "6": "Record_Retrieval",
    "7": "Urgency_Assessment"
  },
  "label2id": {
    "Symptom_Reporting": 0,
    "Advice_Seeking": 1,
    "Medication_Query": 2,
    "Appointment_Scheduling": 3,
    "Billing_Query": 4,
    "Causal_Query": 5,
    "Record_Retrieval": 6,
    "Urgency_Assessment": 7
  },
  "num_labels": 8,
  "problem_type": "single_label_classification",
  "transformers_version": "4.36.0"
}