tinyml2021: Tweak proposals after feedback

jonnor · Nov 2, 2020 · c2a0d5e · c2a0d5e
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diff --git a/tinyml2021/README.md b/tinyml2021/README.md
@@ -107,14 +107,16 @@ Software memory optimizations
 Jon Nordby is a Machine Learning Engineer
 with 10 years of experience developing software for embedded systems
 and data processing of audio and images.
-He holds a Bachelor in Electronics Engineering from 2010,
-and a Master in Data Science from 2019.
+He holds a Bachelor's degree in Electronics Engineering from 2010,
+and a Master's degree in Data Science from 2019.
 His specialization is machine learning for audio and sensor data.
 Since 2019 he is the CTO of Soundsensing,
 a leading provider of IoT sensor systems
 using sound as the primary data source.
 Their systems are used for Noise Monitoring
 and Condition Monitoring of machinery.
+Jon is also the creator of emlearn,
+an open-source machine learning toolkit for microcontrollers and embedded devices.
 
 
 
diff --git a/tinyml2021/coffeecracking.md b/tinyml2021/coffeecracking.md
@@ -11,9 +11,9 @@ The system uses a MEMS microphone to pick up the sound of the roasting process,
 and a Machine Learning model to detect in real-time the sound of coffee beans popping ("cracking").
 The model runs on a custom developed board using an STM32 microcontroller
 and the X-CUBE-AI machine learning toolkit.
-Since August 2020 the product has been shipping for the Roest coffee machines.
+Roest coffee machines have been shipping with this TinyML-powered feature since August 2020.
 
-We will cover the development process from the start and to a validated TinyML-powered solution,
+We will cover the development process from the start and all the way to a validated solution,
 including system design, data collection, model development, and testing.
 
 Roest is a leading manufacturer of coffee-roasting machines.
@@ -40,9 +40,12 @@ Did you know that roasting coffee beans make them crack like popcorn?
 
 During the roasting process the coffebeans heat up and
 the water inside turns to steam.
-This can be released as , resulting in an audible "crack".
+This can be released abruptly through the hard shell,
+resulting in an audible "crack".
 
 Experienced coffee roasters often listen for these cracks.
-But this requires constant virgilance
+But this requires constant virgilance,
+making it hard to do other tasks at the same time.
 
 But how do coffee-makers select their coffee?
+Testing the coffe. Roasting chart.
diff --git a/tinyml2021/environmentalsoundclassification.md b/tinyml2021/environmentalsoundclassification.md
@@ -7,7 +7,7 @@ Noise is a growing problem in urban areas, and according to the WHO is the secon
 Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems.
 It is desirable that these sensor systems, in addition to logging the sound level, can indicate what the likely sound source is.
 Performing such Environmental Noise Classification directly in the sensor is desirable in order to
-avoid sending audio data to the cloud, which may have negative impacts on privacy and battery lifetime.
+avoid sending audio data to the cloud, which may have negative impacts on data transfer amounts, battery lifetime and privacy.
 
 In this talk we will explain how we tested several different
 Convolutional Neural Networks for this task on the STM32L476 low-power microcontroller,
@@ -23,9 +23,10 @@ and we will share some of the progress and challenges of bringing this kind of r
 ## Call-to-Action
 
 Want to test out Machine Learning for Noise Monitoring?
-Use Soundsensing dB20,
-and become a pilot program.
 Be it in cities, residential areas, offices, or factory floors.
+Order the Soundsensing dB20 devkit,
+and join our pilot program for Noise Classification.
+
 
 
 ## Has talk been given before