AI Call Audio Personalization Using Voice Feature Equalization

The system performs preliminary actions by pre-processing sound signals during calls to extract sound features, pre-classifying far-end devices into categories, and pre-storing multiple equalizer parameter sets corresponding to different device categories. This preparation work is done in advance so that when a call occurs, the system can quickly retrieve and apply the appropriate parameters without complex real-time processing, thus achieving personalization without excessive complexity.

The system implements self-service by automatically extracting sound features from incoming sound signals, autonomously classifying far-end devices based on sound characteristics, and automatically selecting and applying appropriate equalizer parameters without requiring manual user configuration. The machine learning model continuously learns from call data to improve classification accuracy, enabling the system to serve itself and adapt to new devices independently.

The system performs preliminary classification of far-end devices into categories during or after calls, and pre-stores multiple equalizer parameter sets corresponding to different device categories. When a new call occurs, the system quickly matches the far-end device to a known category and retrieves the pre-prepared parameter set, avoiding the need for complex real-time machine learning processing during the call itself. This reduces processing time while maintaining sound quality optimization.

The system applies partial action by focusing machine learning processing only on extracting key sound features and classifying device categories, rather than performing exhaustive analysis of all audio parameters. The model processes only the most critical features needed for classification, and uses these partial results to select from pre-prepared parameter sets, achieving adequate sound quality optimization with reduced computational overhead and processing time.