Audio Data Labeler for ML Model Training
AudioClassification
Labeled and processed 213 audio samples to ensure data quality and consistency for effective model training. Evaluated model performance using multiple metrics, identifying failure cases for further refinement. Documented methodology and reasoning behind the labeling and evaluation process for transparency and reproducibility.
• Ensured high-quality audio annotation for training a hybrid CNN classification model.
• Conducted careful preprocessing and manual review of audio data prior to labeling.
• Collaborated with team to implement improvements based on labeling outcomes.
• Contributed to iterative refinement of model through informed data labeling decisions.
Labeled and processed 213 audio samples to ensure data quality and consistency for effective model training. Evaluated model performance using multiple metrics, identifying failure cases for further refinement. Documented methodology and reasoning behind the labeling and evaluation process for transparency and reproducibility. • Ensured high-quality audio annotation for training a hybrid CNN classification model. • Conducted careful preprocessing and manual review of audio data prior to labeling. • Collaborated with team to implement improvements based on labeling outcomes. • Contributed to iterative refinement of model through informed data labeling decisions.
2024 - 2024