Passive Radiator Excursion Prediction to Prevent Audio Clipping
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Solution Overview
Problem
Audio playback systems with passive radiators face distortion due to clipping when the voltage applied exceeds the excursion limits, leading to audible distortion and signal cutting off, which is not effectively addressed by existing technologies.
Innovation Solution
Implementing a forward prediction model to predict the excursion of a passive radiator caused by active speakers, modifying audio content to limit excursion within safe limits, and using feedback to adjust the model for accurate prediction and minimize distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If voltage is increased to improve audio output, then sound pressure level increases, but passive radiator excursion exceeds physical limits causing clipping distortion
Solution Approach 1:
The system performs preliminary prediction of passive radiator excursion using a forward prediction model before actual playback. By analyzing the audio signal in advance and predicting the excursion that would result from applying a given voltage, the system can preemptively adjust the voltage to prevent clipping distortion before it occurs.
Solution Approach 2:
The system measures actual passive radiator excursion during playback and uses this feedback to adjust the forward prediction model. By comparing predicted excursion with measured excursion, the model parameters are refined to improve prediction accuracy, enabling more reliable prevention of clipping distortion in future playback scenarios.
2Reliability
If excursion limit is reduced to prevent clipping, then distortion is minimized, but audio output power decreases
Solution Approach 1:
The system dynamically adjusts the voltage applied to active speakers based on real-time prediction of passive radiator excursion. Rather than using a fixed conservative voltage limit, the system continuously monitors the audio signal and adjusts voltage levels to maximize output while staying within the passive radiator's physical excursion limits, thereby optimizing the balance between power and signal quality.
3Measurement precision
If forward prediction model is made more complex to improve prediction accuracy, then excursion prediction precision increases, but device complexity increases
Solution Approach 1:
The system models the relationship between voltage and passive radiator excursion using a linear relationship with frequency-dependent parameters. By characterizing the system at different frequencies and using these frequency-dependent parameters in the prediction model, the system achieves accurate excursion prediction while maintaining computational efficiency and avoiding overly complex modeling approaches.
Data Source
AI summary
Example techniques may involve controlling a passive radiator. An implementation may include a device playing back an input signal representing audio content via one or more active speakers. The device measures excursion of the passive radiator when the input signal is played back via the one or more active speakers. The device limits excursion of the passive radiator to less than an excursion limit when certain input causes the passive radiator to move beyond the excursion limit.