Environmental sound recognition on embedded systems: From FPGAs to TPUs
In recent years, Environmental Sound Recognition (ESR) has become a relevant capability
for urban monitoring applications. The techniques for automated sound recognition often
rely on machine learning approaches, which have increased in complexity in order to
achieve higher accuracy. Nonetheless, such machine learning techniques often have to be
deployed on resource and power-constrained embedded devices, which has become a
challenge with the adoption of deep learning approaches based on Convolutional Neural …
for urban monitoring applications. The techniques for automated sound recognition often
rely on machine learning approaches, which have increased in complexity in order to
achieve higher accuracy. Nonetheless, such machine learning techniques often have to be
deployed on resource and power-constrained embedded devices, which has become a
challenge with the adoption of deep learning approaches based on Convolutional Neural …
[PDF][PDF] Environmental Sound Recognition on Embedded Systems: From FPGAs to TPUs. Electronics 2021, 10, 2622
J Vandendriessche, N Wouters, B da Silva, M Lamrini… - 2021 - academia.edu
In recent years, Environmental Sound Recognition (ESR) has become a relevant capability
for urban monitoring applications. The techniques for automated sound recognition often
rely on machine learning approaches, which have increased in complexity in order to
achieve higher accuracy. Nonetheless, such machine learning techniques often have to be
deployed on resource and power-constrained embedded devices, which has become a
challenge with the adoption of deep learning approaches based on Convolutional Neural …
for urban monitoring applications. The techniques for automated sound recognition often
rely on machine learning approaches, which have increased in complexity in order to
achieve higher accuracy. Nonetheless, such machine learning techniques often have to be
deployed on resource and power-constrained embedded devices, which has become a
challenge with the adoption of deep learning approaches based on Convolutional Neural …
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