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Animal_Sound_Classification

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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      'str' object is not a mapping
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 996, in dataset_module_factory
                  return HubDatasetModuleFactory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2027, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2023, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2023, in <dictcomp>
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2020, in from_yaml_inner
                  return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
              TypeError: 'str' object is not a mapping

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🐾 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.

  • Curated by: Muhammad Qasim
  • Funded by: Self-initiated Open-Source Project
  • License: MIT License
  • Language(s): Non-linguistic (animal sounds)

🔗 Dataset Sources

🚀 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

Data Instances

Field Name Type Description
filename string Name of the audio file
mfcc_1 float64 First MFCC feature
mfcc_2 float64 Second MFCC feature
... ... ...
mfcc_13 float64 Thirteenth MFCC feature

Data Fields

  • filename: Name of the audio file.
  • mfcc_1 to mfcc_13: Mel-frequency cepstral coefficients (MFCCs) extracted from the audio files.

Data Splits

Split Number of Examples Total Size
Train 1045 114.4 KB

📥 Dataset Creation

Curation Rationale

The dataset was created to facilitate research and development in the field of audio classification, particularly focusing on animal sounds. The goal is to provide a high-quality, ready-to-use dataset for machine learning practitioners and researchers.

Source Data

Initial Data Collection and Normalization

  • Data Collection: Audio clips were collected from various sources and manually trimmed to isolate individual animal sounds.
  • Annotations: Each audio clip was labeled with the corresponding animal class.
  • Who are the annotators? The annotations were generated by an expert.

Personal and Sensitive Information

The dataset does not contain any personal or sensitive information.

📜 Additional Information

Dataset Curators

Muhammad Qasim

Licensing Information

MIT License

Citation Information

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