report: Grammar check abstract

Author: jonnor

report/abstract.latex

@@ -7,11 +7,11 @@
 
 Noise is a growing problem in urban areas,
 and according to the WHO is the second environmental cause of health problems in Europe.
-Noise monitoring using Wireless Sensor Networks is
+Noise monitoring using Wireless Sensor Networks are
 being applied in order to understand and help mitigate these noise problems.
-It is desireable that these sensor systems, in addition to logging the sound level,
+It is desirable that these sensor systems, in addition to logging the sound level,
 can indicate what the likely sound source is.
-However transmitting audio to a cloud system for classification is
+However, transmitting audio to a cloud system for classification is
 energy-intensive and may cause privacy issues.
 It is also critical for widespread adoption and dense sensor coverage that
 individual sensor nodes are low-cost.
@@ -21,15 +21,15 @@ using a low-cost microcontroller.
 Several Convolutional Neural Networks were designed for the
 STM32L476 low-power microcontroller using the Keras deep-learning framework,
 and deployed using the vendor-provided X-CUBE-AI inference engine.
-The resource budget for the model was set at maximum 50\% utilization of CPU, RAM and FLASH.
+The resource budget for the model was set at maximum 50\% utilization of CPU, RAM, and FLASH.
 10 model variations were evaluated on the Environmental Sound Classification task
 using the standard Urbansound8k dataset.
 
 The best models used Depthwise-Separable convolutions with striding for downsampling,
 and were able to reach 70.9\% mean 10-fold accuracy while consuming only 20\% CPU.
 To our knowledge, this is the highest reported performance on Urbansound8k using a microcontroller.
-One of the models was also tested on device,
-demonstrating classification of environmental sounds in real-time.
+One of the models was also tested on a microcontroller development device,
+demonstrating the classification of environmental sounds in real-time.
 
 These results indicate that it is computationally feasible to classify environmental sound
 on low-power microcontrollers.