Equalization of excursion and current-dependent nonlinearities in loudspeakers

pre-printThis paper presents a novel equalizer for nonlinear distortions in direct-radiator loudspeakers in a closed cabinet by constructing an exact inverse of an electro-mechanical model of the loudspeaker. This exact inverse compensates for distortions introduced by excursion and current-dependent nonlinearities. The equalizer compensates for the nonlinearities in the force factor, voice coil inductance, eddy currents and the stiffness of the loudspeaker. Simulation results demonstrating substantial reduction in the harmonic distortions at the output of the loudspeaker are included in this paper

Electrodynamic loudspeaker linearization using a low complexity pth-Order inverse nonlinear filter

Nonlinear distortions are very challenging to tackle in electromechanical loudspeakers. They are observed in large signals mode, where high amplitude stimulus drives different components of the transducer to operate in their nonlinear region, resulting in harmonic and intermodulation distortions in the reproduced sounds. Many linearization schemes have been proposed to address this problem, they operate by pre-distorting the input signal before exciting the loudspeaker, in the aim of radiating distortion-free sound waves. In this work, we are interested in the performance evaluation of a low computational complexity feedforward linearization structure which is based on the pth order inverse of a one-dimension Volterra model of the driver. The scheme is designed to compensate for the 2nd and 3rd harmonic distortions. We will study the effect of varying the input voltage amplitude on the harmonic distortions reduction performance. A lumped-parameters model with parameters of a real driver will be used for the evaluation

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A room acoustics measurement system using non-invasive microphone arrays

This thesis summarises research into adaptive room correction for small rooms and pre-recorded material, for example music of films. A measurement system to predict the sound at a remote location within a room, without a microphone at that location was investigated. This would allow the sound within a room to be adaptively manipulated to ensure that all listeners received optimum sound, therefore increasing their enjoyment. The solution presented used small microphone arrays, mounted on the room's walls. A unique geometry and processing system was designed, incorporating three processing stages, temporal, spatial and spectral. The temporal processing identifies individual reflection arrival times from the recorded data. Spatial processing estimates the angles of arrival of the reflections so that the three-dimensional coordinates of the reflections' origin can be calculated. The spectral processing then estimates the frequency response of the reflection. These estimates allow a mathematical model of the room to be calculated, based on the acoustic measurements made in the actual room. The model can then be used to predict the sound at different locations within the room. A simulated model of a room was produced to allow fast development of algorithms. Measurements in real rooms were then conducted and analysed to verify the theoretical models developed and to aid further development of the system. Results from these measurements and simulations, for each processing stage are presented