Whisper Variants For Beginners

This is a series of beginners friendly videos targeting some of the available variants of OpenAI whisper model for Speech to text transcription, and the steps required to install such variants. We walk through these steps to get all the requirements ready, enable GPU calculations and transcribe a few example audio files.

Radar-Based Materials Detection

Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance and control. Several radar publications were developed for material classification under controlled settings with specific materials' properties and shapes. Recent literature has challenged the earlier findings on radars-based materials classification claiming that earlier solutions are not easily scaled to industrial applications due to a variety of real-world issues. Published experiments on the impact of these factors on the robustness of the extracted radar-based traditional features have already demonstrated that the application of deep neural networks can mitigate, to some extent, the impact to produce a viable solution. However, previous studies lacked an investigation of the usefulness of lower frequency radar units, specifically <10GHz, against the higher range units around and above 60GHz. This research considers two radar units with different frequency ranges: Walabot-3D (6.3-8 GHz) cm-wave and IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging. Read the full paper below

Paper URL: https://arxiv.org/abs/2202.05169

Radar-based Materials Classification work featured on the ACFR channel

Tutorials: Signal Processing and Machine Learning