Part 2: The Data — Building the First Public Coffee Roasting Audio Dataset with Warp/Oz

1. 1Record audio sessions of coffee roasting to collect data
2. 2Annotate audio files in Label Studio to label first crack events
3. 3Design a pipeline to process and prepare the audio data for model training
4. 4Implement data augmentation techniques to increase dataset size and diversity
5. 5Configure a data pipeline to avoid common failure modes in time-series data

Data scientists and machine learning engineers can benefit from this tutorial to improve their skills in building datasets and pipelines for audio classification tasks, while product managers can apply this knowledge to develop more accurate coffee roasting detection models

💡 Building a high-quality dataset is crucial for training accurate machine learning models, especially in audio classification tasks

📊 Build a public audio dataset for coffee roasting first crack detection and improve your ML model's accuracy #MachineLearning #AudioClassification