Abstract: Various implementations include electronic devices with internal joints cured by a light source. In a particular implementation, an electronic device includes: a casing defining an enclosure; a joint between components in the electronic device; a light curable adhesive bonding the components at the joint; and at least one light source internal to the casing and configured to output light for at least partially curing the light curable adhesive in the casing.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to characterize a malfunction in response to instability or error condition events detected by the second DSP.

Abstract: Various implementations include a method for implementing a computational architecture for a personal active noise reduction (ANR) device. A method includes receiving a source audio stream with a first DSP and performing ANR on the source audio stream utilizing operational parameters stored in the first DSP; outputting a processed audio stream from the first DSP; generating state data with a second DSP in response to an analysis of at least one of the source audio stream, microphone inputs and the processed audio stream, and communicating signals to the first DSP over a common bus coupled to the first and second DSPs to alter the operational parameters in the first DSP; and utilizing a general purpose processor coupled to both the first DSP and the second DSP to communicate control signals with a communication interface, process state data from the second DSP, and alter the operational parameters in the first DSP.

Abstract: Various implementations include a method for implementing a computational architecture for a personal active noise reduction (ANR) device. A method includes receiving a source audio stream with a first DSP and performing ANR on the source audio stream utilizing operational parameters stored in the first DSP; outputting a processed audio stream from the first DSP; generating state data with a second DSP in response to an analysis of at least one of the source audio stream, microphone inputs and the processed audio stream, and communicating signals to the first DSP over a common bus coupled to the first and second DSPs to alter the operational parameters in the first DSP; and utilizing a general purpose processor coupled to both the first DSP and the second DSP to communicate control signals with a communication interface, process state data from the second DSP, and alter the operational parameters in the first DSP.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.

Abstract: Various implementations include a computational architecture for a personal active noise reduction (ANR) device. The device includes a communication interface that receives an audio stream, a driver, a microphone system and an ANR processing platform. The platform includes a first DSP configured to: receive the audio stream and signals from the microphone system, perform ANR on the audio stream according to a set of parameters in the first DSP, and output a processed audio stream. The platform includes a second DSP configured to: generate state data in response to an analysis of the source audio stream, signals from the microphone system, and the processed audio stream; and alter the operational parameters on the first DSP. The platform includes a general purpose processor configured to: communicate control signals with the communication interface, process state data from the second DSP, and alter the parameters on the first DSP.

Abstract: Various implementations include approaches for dynamically demonstrating device capabilities in a wearable audio device based upon detected environmental conditions. In certain cases, these approaches include initiating a demonstration using the wearable audio device to provide the user with an example of the device capabilities, based upon detected environmental conditions proximate the wearable audio device and/or a biometric state of the device user.

Abstract: Various implementations include approaches for dynamically demonstrating device capabilities in a wearable audio device based upon detected environmental conditions. In certain cases, these approaches include initiating a demonstration using the wearable audio device to provide the user with an example of the device capabilities, based upon detected environmental conditions proximate the wearable audio device and/or a biometric state of the device user.