ML Audio Data Labeling & Training Project
AudioClassification
Worked on training and evaluating a hybrid CNN audio classification model using labeled audio data. Ensured high data quality and consistency by processing and labeling a dataset of 213 audio samples for machine learning purposes. Evaluated the model using accuracy, precision, and recall while identifying and improving failure cases.
• Labeled and processed 213 audio samples for supervised model training
• Documented training methodology and evaluation process in detail
• Improved dataset labeling through iterative quality checks
• Ensured reliable and consistent annotations throughout project
Worked on training and evaluating a hybrid CNN audio classification model using labeled audio data. Ensured high data quality and consistency by processing and labeling a dataset of 213 audio samples for machine learning purposes. Evaluated the model using accuracy, precision, and recall while identifying and improving failure cases. • Labeled and processed 213 audio samples for supervised model training • Documented training methodology and evaluation process in detail • Improved dataset labeling through iterative quality checks • Ensured reliable and consistent annotations throughout project
2024 - 2024