AES 5th International Conference on Automotive Audio

This paper introduces a novel approach to enhancing loudspeaker performance by employing Long Short-Term Memory (LSTM) neural networks to linearize the driving force on the voice coil. The method enables transducer engineers to design more cost-effective drivers by prioritizing the optimization of mechanical components (such as the membrane and suspension), while allowing for greater compromises and flexibility in the design of the magnetic system (including the voice coil and motor), whose deficiencies are compensated by the controller. This flexibility is particularly beneficial in challenging applications where size, cost, and weight constraints are significant factors, such as in automotive and portable devices.

We propose a control algorithm that linearizes the driving force by adjusting the voice coil current and compensating for the force factor BL nonlinearity. By focusing on the current signal, which is readily obtainable, and the BL nonlinearity, which is relatively stable and predictable, our controller architecture remains practical and robust. This intentional avoidance of complex and potentially time-varying mechanical nonlinearities and the associated costly acquisition of mechanical signals (e.g., displacement, velocity, and acceleration) ensures feasibility, especially in production environments.

The performance of the controller was rigorously evaluated using a real 2.5-inch driver deliberately engineered with compromised voice coil characteristics, resulting in a distorted BL curve and heightened levels of distortion. Comparative analysis was conducted against a non-controlled sample of a similar driver with a properly designed magnetic system (i.e., both drivers sharing the same mechanical system, but the controlled unit exhibiting suboptimal magnetic properties). Our proposed controller algorithm achieved approximately 10 dB reduction in distortion above the resonance region, approaching the performance of the well-designed unit.

ID:03_Volkov