--Numerous tools for neural network inference are currently available, yet many do not meet the requirements of real-time audio applications. In response, we introduce anira, an efficient cross-platform library. T o ensure compatibility with a broad range of neural network architectures and frameworks, anira supports ONNX Runtime, LibT orch, and T ensorFlow Lite as backends. Each inference engine exhibits real-time violations, which anira mitigates by decoupling the inference from the audio callback to a static thread pool. The library incorporates built-in latency management and extensive benchmarking capabilities, both crucial to ensure a continuous signal flow. Three different neural network architectures for audio effect emulation are then subjected to benchmarking across various configurations. Statistical modeling is employed to identify the influence of various factors on performance. The findings indicate that for stateless models, ONNX Runtime exhibits the lowest runtimes. For stateful models, LibT orch demonstrates the fastest performance. Our results also indicate that for certain model-engine combinations, the initial inferences take longer, particularly when these inferences exhibit a higher incidence of real-time violations. In recent years, neural networks have become an integral part of modern audio digital signal processing. Their applications include audio classification [1], audio transcription [2], audio source separation [3], audio synthesis [4], [5], [6] and audio effects [7]. While offline processing is inherently supported, translating these architectures to real-time implementations remains challenging.

The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective monitoring systems. In this paper, we present MeciFace, an innovative wearable technology designed to monitor facial expressions and eating activities in real-time on-the-edge (RTE). MeciFace aims to provide a low-power, privacy-conscious, and highly accurate tool for promoting healthy eating behaviors and stress management. We employ lightweight convolutional neural networks as backbone models for facial expression and eating monitoring scenarios. The MeciFace system ensures efficient data processing with a tiny memory footprint, ranging from 11KB to 19KB. During RTE evaluation, the system achieves impressive performance, yielding an F1-score of < 86% for facial expression recognition and 90% for eating/drinking monitoring, even for the RTE of an unseen user.

There are many emerging trends in the tech world, and Machine Learning is one of them. Machine Learning is a subset of Artificial Intelligence where a computer learns from data and analyses its patterns to predict an outcome. Usually, Machine Learning models are trained on big chunks of data to analyze the patterns where these complex models require hours or even days to get processed in the cloud centers. The resultant file of these models also contains a good amount of data. As we all know, data is constantly flowing.

Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection system. However, many applications require low-cost solutions, composed of cheap sensors and simple microcontrollers. In this paper, we demonstrate that even on such hardware we are not condemned to simple classic image processing techniques. We propose a novel ultra-lightweight CNN-based person detector that processes thermal video from a low-cost 32x24 pixel static imager. Trained and compressed on our own recorded dataset, our model achieves up to 91.62% accuracy (F1-score), has less than 10k parameters, and runs as fast as 87ms and 46ms on low-cost microcontrollers STM32F407 and STM32F746, respectively.

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you'll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data, and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.

Imagine if your smart speaker could be trained to recognize your accent, or if a pair of running shoes could alert you in real time if your gait changed, indicating fatigue. Or if, in the industrial world, sensors could parse vibration information from a machine that changed location and function often in real time, halting the machine if that information suggested there was a problem. We often write about the value of on-device machine learning (ML), but what we're generally discussing is running existing models on a device and matching incoming data against the established model. This is known as inference. So when you say the name "Alexa," your smart speaker matches the pattern and wakes up.

TensorFlow Lite is an open source deep learning framework for on-device inference. This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data . We will start with trigonometric functions approximation . Next is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classification Another amazing project is focused on convolution network but the data is custom voice recordings .

Introducing TinyML, a state-of-the-art field that brings the performative power of ML to shrink deep structured earning networks to fit on tiny hardware. It is a new approach to edge computing that investigates the deployment and training of machine learning models on edge devices. TinyML is right at the intersection between embedded machine learning applications, hardware, software, and algorithms. It is an intersection of embedded systems and regular machine learning. It demands not just software expertise but also demands expertise in embedded systems – both of which have significant challenges of their own.

Deep learning is changing our lives in small and large ways every day. Whether it's Siri or Alexa following our voice commands, the real-time translation apps on our phones, or the computer vision technology enabling smart tractors, warehouse robots, and self-driving cars, every month seems to bring new advances. And almost all of these deep learning applications are written in one of three frameworks: TensorFlow, PyTorch, and JAX. Which of these deep learning frameworks should you use? In this article, we'll take a high-level comparative look at TensorFlow, PyTorch, and JAX. We'll aim to give you some idea of the types of applications that play to their strengths, as well as consider factors like community support and ease-of-use.

Machine learning (ML) is a growing field, gaining popularity in academia, industry, and among makers. We will take a look at some of the available tools to help make machine learning easier, but first, let's review some of the terms commonly used in machine learning. John McCarthy provides a definition of artificial intelligence (AI) in his 2007 Stanford paper, "What is Artificial Intelligence?" In it, he says AI "is the science and engineering of making intelligent machines, especially intelligent computer programs." This definition is extremely broad, as McCarthy defines intelligence as "the computational part of the ability to achieve goals in the world." As a result, any program that achieves some goal can easily be classified as artificial intelligence. In her article "Machine Learning on Microcontrollers" (Make: Vol.