七麦数据

Build custom audio classification models directly on your iPhone.

Annotate Audio is a complete machine learning toolkit for developers, researchers, and anyone looking to use audio classification who need to collect, annotate, and train audio classification systems. Turn your device into a powerful data collection and simple model training platform.

CUSTOM MODEL TRAINING
Train your own Core ML sound classification models on-device using your recorded audio data. Perfect for developers building audio-aware applications or researchers creating specialized datasets.

INTELLIGENT AUTO-RECORDING
Deploy your trained models to automatically detect and record target sounds. Your basic models help collect more training data, creating a self-improving cycle that enhances classification accuracy over time.

PROFESSIONAL DATA COLLECTION
• Real-time waveform visualization during recording
• Manual and automatic recording modes
• Background recording with proper iOS session management
• Organized file management with classification-based folders

SEAMLESS MODEL EXPORT & SYNC
• Export trained models for use in other applications
• iCloud synchronization across devices
• Transfer datasets to more powerful training environments
• Compatible with professional ML workflows

EXAMPLE USE CASES
• Industrial sound monitoring and machinery diagnostics
• Bioacoustics research and animal call detection
• Environmental audio analysis
• Speech pattern recognition
• Custom audio trigger systems
• IoT and smart home audio classification

Built with SwiftUI and optimized for performance with aggressive memory management, background processing, and professional audio session handling. Uses CreateML for on-device training and Core ML for real-time inference.

Whether you're prototyping an audio-aware app, collecting research data, or building custom sound detection systems, Annotate Audio provides the complete pipeline from initial recording to deployed classification models.

Requires iOS device for model training. Export capabilities enable scaling to more powerful training environments.

Build custom audio classification models directly on your iPhone.

Annotate Audio is a complete machine learning toolkit for developers, researchers, and anyone looking to use audio classification who need to collect, annotate, and train audio classification systems. Turn your device into a powerful data collection and simple model training platform.

CUSTOM MODEL TRAINING
Train your own Core ML sound classification models on-device using your recorded audio data. Perfect for developers building audio-aware applications or researchers creating specialized datasets.

INTELLIGENT AUTO-RECORDING
Deploy your trained models to automatically detect and record target sounds. Your basic models help collect more training data, creating a self-improving cycle that enhances classification accuracy over time.

PROFESSIONAL DATA COLLECTION
• Real-time waveform visualization during recording
• Manual and automatic recording modes
• Background recording with proper iOS session management
• Organized file management with classification-based folders

SEAMLESS MODEL EXPORT & SYNC
• Export trained models for use in other applications
• iCloud synchronization across devices
• Transfer datasets to more powerful training environments
• Compatible with professional ML workflows

EXAMPLE USE CASES
• Industrial sound monitoring and machinery diagnostics
• Bioacoustics research and animal call detection
• Environmental audio analysis
• Speech pattern recognition
• Custom audio trigger systems
• IoT and smart home audio classification

Built with SwiftUI and optimized for performance with aggressive memory management, background processing, and professional audio session handling. Uses CreateML for on-device training and Core ML for real-time inference.

Whether you're prototyping an audio-aware app, collecting research data, or building custom sound detection systems, Annotate Audio provides the complete pipeline from initial recording to deployed classification models.

Requires iOS device for model training. Export capabilities enable scaling to more powerful training environments.