--We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples. In this paper, we first create a new dataset with over one hour of audio data that enables our research and will be useful to future studies in this field. Second, we utilize the technologies provided by Edge Impulse to enhance our model's performance and achieve a high Accuracy of up to 97% on our dataset. For the validation, we implement our prototype using the Arduino Nano 33 BLE Sense microcontroller board. This microcontroller board is specifically designed for IoT and AI applications, making it an ideal choice for our target use case scenarios. While most existing research focuses on a limited set of keywords, our model can process 23 different keywords, enabling complex commands. Natural Language Processing (NLP) and Speech Recognition are crucial domains in Artificial Intelligence (AI). While NLP deals with enabling computers to analyze, understand, reason on, and generate human language in textual form, speech recognition is concerned with that in spoken form.

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real - time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real - time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno - canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel - Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real - world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation.

In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. The gesture recognition,provides an innovative approach nonverbal communication. It has wide applications in human-computer interaction and sign language. Here in the implementation of hand gesture recognition, TinyML model is trained and deployed from EdgeImpulse framework for hand gesture recognition and based on the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out the direction of movement of hand. The Speech is a mode of communication. Speech recognition is a way by which the statements or commands of human speech is understood by the computer which reacts accordingly. The main aim of speech recognition is to achieve communication between man and machine. Here in the implementation of speech recognition, TinyML model is trained and deployed from EdgeImpulse framework for speech recognition and based on the keywords pronounced by human, Arduino Nano 33 BLE device having built-in microphone can make an RGB LED glow like red, green or blue based on keyword pronounced. The results of each application are obtained and listed in the results section and given the analysis upon the results.

We performed live classification of data using both our smartphones as well as the Arduino Nano 33 BLE Sense. Every Edge Impulse project has a test dataset in addition to its training data. The test dataset is immediately saved with the samples taken in Live classification, and the Model testing page displays all of the test data. To use the sample that was captured for testing, the expected outcome should be edited accordingly. Click the icon and select Edit expected outcome, then enter the relevant label, as shown below. Now, select the sample using the checkbox to the left of the table and click Classify selected. We can observe that the model's accuracy has been rated based on the test data. As expected, the performance of the model isn't always great on the first attempt, which can be so due to several factors.

Machine learning is getting lots of attention in the maker community, expanding outward from the realms of academia and industry and making its way into DIY projects. With traditional programming you explicitly tell a computer what it needs to do using code; with machine learning the computer finds its own solution to a problem, based on examples you've shown it. You can use machine learning to work with complex datasets that would be very difficult to hard-code, and the computer can find connections you might miss! How does machine learning (ML) actually work? Let's use the classic example: training a machine to recognize the difference between pictures of cats and dogs. Imagine that a small child, with an adult, is looking at a book full of pictures of cats and dogs.

In this tutorial, we want to cover how to get started with Arduino Nano 33 BLE Machine Learning using Edge Impulse. We want to spot some basic keywords using machine learning and Arduino. In this tutorial, we will create a custom dataset using Edge Impulse to collect data. At the end of this post, you will be able to deploy a machine learning model on your Arduino Nano 33 BLE. It can spot some custom keywords such as color names so that we can control an RGB WS2812 strip using voice commands.

If you're interested in embedded machine learning (TinyML) on the Arduino Nano 33 BLE Sense, you'll have found a ton of on-board sensors -- digital microphone, accelerometer, gyro, magnetometer, light, proximity, temperature, humidity and color -- but realized that for vision you need to attach an external camera. In this article, we will show you how to get image data from a low-cost VGA camera module. We'll be using the Arduino_OVD767x library to make the software side of things simpler. You can of course get a board without headers and solder instead, if that's your preference. The one downside to this setup is that (in module form) there are a lot of jumpers to connect.

This tutorial covers how to use Machine Learning with Arduino. The aim of this tutorial is to build a voice controlled car from scratch that uses Tensorflow Machine Learning to recognize voice commands. To do it we will use Arduino Nano 33 BLE sense. The availability of the Tensorflow lite for microcontrollers makes it possible to run machine learning algorithms on microcontrollers such as Arduino. In this tutorial, we will build a Tensorflow model that recognizes voice commands.

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog. Arduino is on a mission to make machine learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.

Arduino is on a mission to make Machine Learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands. Next, we'll introduce a more in-depth tutorial you can use to train your own custom gesture recognition model for Arduino using TensorFlow in Colab.