831 research outputs found
Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments
State-of-the-art deep-learning-based voice activity detectors (VADs) are
often trained with anechoic data. However, real acoustic environments are
generally reverberant, which causes the performance to significantly
deteriorate. To mitigate this mismatch between training data and real data, we
simulate an augmented training set that contains nearly five million
utterances. This extension comprises of anechoic utterances and their
reverberant modifications, generated by convolutions of the anechoic utterances
with a variety of room impulse responses (RIRs). We consider five different
models to generate RIRs, and five different VADs that are trained with the
augmented training set. We test all trained systems in three different real
reverberant environments. Experimental results show increase on average
in accuracy, precision and recall for all detectors and response models,
compared to anechoic training. Furthermore, one of the RIR models consistently
yields better performance than the other models, for all the tested VADs.
Additionally, one of the VADs consistently outperformed the other VADs in all
experiments.Comment: Accepted to ICASSP 202
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192
This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979
Low-Power and Programmable Analog Circuitry for Wireless Sensors
Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits
Low-Power and Programmable Analog Circuitry for Wireless Sensors
Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits
- …