---
dataset:
  name: Animal Sound Classification Dataset
  dataset_type: audio-classification
  license: mit
  annotations_creators:
  - expert-generated
  language:
  - no-linguistic-content
  task_categories:
  - audio-classification
  pretty_name: Animal Sound Classification
  size_categories:
  - 1<n<1.1K
  tags:
  - animal-sounds
  - audio
  - sound-classification
  - environmental-sounds
  - MFCC
  - open-dataset
  - sound-recognition
  dataset_info:
    features:
    - name: audio
      type: audio
    - name: label
      type: string
    splits:
    - name: train
      num_bytes:
      - TO_BE_FILLED
      num_examples:
      - TO_BE_FILLED
  creators:
  - name: Muhammad Qasim
    url: https://github.com/MuhammadQasim111
license: mit
pretty_name: ANIMAL_SOUND_CLASSIFICATIO
---

# 🐾 Animal Sound Classification Dataset

> **A meticulously handcrafted dataset of labeled animal sounds for Machine Learning & Audio Classification tasks.**  
> **Built with love, precision, and open-source spirit.**

---

## 📖 Dataset Details

### 📝 Dataset Description
The **Animal Sound Classification Dataset** contains curated audio clips of **dogs, cats, cows**, and more, extracted from longer recordings and meticulously trimmed to create clean, high-quality sound samples.

Over a period of **two months**, I manually processed, trimmed, and labeled each audio file. I also prepared the dataset for ML pipelines by extracting **MFCC (Mel-Frequency Cepstral Coefficients)** features to ensure seamless integration for developers and researchers.
ALL THE HECTIC WORK OF MINE, IS SERVED TO YOU ON DISH, FOR FREE OF COST
- **Curated by:** Muhammad Qasim
- **Funded by:** Self-initiated Open-Source Project
- **License:** MIT License
- **Language(s):** Non-linguistic (animal sounds)

---

## 🔗 Dataset Sources

- **Repository:** [Hugging Face Link](https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification)
  

---

## 🚀 Uses

### ✅ Direct Use
- Audio classification model training.
- Sound recognition AI systems.
- Educational apps that teach animal sounds.
- Wildlife and livestock sound monitoring AI.

### 🚫 Out-of-Scope Use
- Speech Recognition tasks.
- Use in sensitive environments without proper augmentation.
- Misuse for deceptive simulations.

---

## 🗂️ Dataset Structure

| Field Name | Type   | Description                        |
|------------|--------|------------------------------------|
| audio      | Audio  | The sound clip (.wav file)         |
| label      | String | Animal class label (e.g., "dog")   |

### Folder Structure
```bash
animal-sounds-dataset/
├── data/
│   ├── dog_bark_1.wav
│   ├── cat_meow_2.wav
│   ├── cow_moo_3.wav
│   
├── dataset.py
├── dataset_infos.json
└── README.md
🎯 Dataset Creation Process
✨ Curation Rationale
I observed a lack of high-quality, open-source datasets specifically for animal sound classification tasks. This dataset bridges that gap by offering ML practitioners a clean, labeled dataset that's ready-to-use.

📥 Data Collection & Processing
Sourced open-access recordings of animal sounds.

Manually trimmed long recordings into focused, high-quality clips.

Normalized sound levels to ensure dataset consistency.

MFCC features extracted to align with audio classification models.

👤 Data Producers
Public open-access sound repositories.

Curation, annotation, and final dataset assembly done by Muhammad Qasim.

🖍️ Annotations
Annotation Process
Manual listening to each clip.

Precise labeling according to animal sound type.

File names follow a structured convention: animal_sound_x.wav.

Annotators
Solely annotated by Muhammad Qasim.

Personal and Sensitive Information
No personal or sensitive information is present in this dataset.

⚠️ Bias, Risks, and Limitations
Limited to common animals: dogs, cats, cows.

May not generalize well to rare animal sounds.

Sound recordings are from clean environments; real-world noisy scenarios may require augmentation.

🔎 Recommendations
For production systems, augment this dataset with diverse environments and more animal classes. Exercise caution when applying in critical systems.

📜 Citation
If you use this dataset, please cite it as:

MuhammadQasim

MuhammadQasim
Copy
Edit
@dataset{qasim2025animalsounds,
  title = {Animal Sound Classification Dataset},
  author = {Muhammad Qasim},
  year = {2025},
  url = {https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification}
}
APA
Muhammad Qasim. (2025). Animal Sound Classification Dataset. Hugging Face. https://huggingface.co/datasets/MuhammadQASIM111/Animal_Sound_Classification

📚 Glossary
MFCC (Mel-Frequency Cepstral Coefficients): A feature widely used in speech and sound processing for its ability to capture the timbral characteristics of sound.

🛠️ More Information
Planned Extensions:

Inclusion of wild animal sounds.

Dataset augmentation with real-world background noise.

Adding more diverse animal types.

✍️ Dataset Card Author
Muhammad Qasim — GitHub Profile

📬 Contact
Email: mqasim111786111@gmail.com

Hugging Face Profile: MuhammadQasim111

<p align="center"> 🚀 *Advancing AI with Open Datasets. Your contributions build the future.* 🚀 </p> ```