I’m excited to share my challenging experience working on my latest project using Edge Impulse. This time, I was on a mission to train a machine learning model to recognise and respond to two core sounds in Egyptian percussion: “Doom” and “Tak.” Spoiler alert: it was a lot tougher than my “Hey Shrouk” project! 

Step 1: Data Collection I started by collecting data for the “Doom” and “Tak” sounds. This involved recording multiple samples of each sound.

Step 2: Dataset Overview With 41 seconds of audio data collected, I split it into training and test sets. This step is for validating the model later on. Each sound (“Doom” and “Tak”) had its dedicated samples labelled for effective training. 

Step 3: Impulse Design Designing the impulse involved setting up time series data, adding a processing block, and configuring the learning block. This setup is the backbone of how the model will interpret and classify the sounds! 🧩

Step 4: Feature Generation Here, I used the Audio (MFE) block to extract Mel-filterbank energy features from the audio signals. These features are essential for distinguishing between “Doom” and “Tak.” This step felt like giving the model its auditory senses 🎧

Step 5: Training the Model Next, I trained the model with the collected data. This part was challenging as I had to tweak parameters like sample rate and noise handling. Despite my efforts, the model sometimes detected a “Tak” when there was none and vice versa, though it handled “Doom” better. 🏋️‍♂️

Step 6: Feature Explorer Visualising the features in the explorer gave me insights into how well the model was learning to differentiate the sounds. The scatter plot shows the clusters of “Doom” and “Tak,” albeit with some overlap due to noise issues. 🔍

Step 7: Neural Network Architecture I configured the neural network with several convolutional layers to process the audio features. This architecture aimed to enhance the model’s ability to recognise intricate patterns in the sounds

Step 8: Model Performance The final model showed an impressive accuracy of 100% on the validation set, but achieving this in real-world conditions proved tricky due to noise and timing issues. The confusion matrix highlights the model’s perfect score on the test data, but it still needs refinement for practical use 🙁

Working on the “Doom” and “Tak” project with Edge Impulse is an enlightening experience. It pushed my boundaries and taught me the importance of fine-tuning parameters to handle real-world complexities. I am also struggling with sample rates  and timings for more accurate representation of a beat. While the journey is a little tough, especially dealing with noise and sample rate issues, the process is still rewarding and I’m super excited to figure out how to solve those issues!

Machine learning is as much an art as it is a science, I will keep experimenting! 💪🏽🥳

In my quest to make my belly dancing robot recognise percussion sounds, I started with a more generic AI sound recognition exercise. And it was super exciting! “Hey Siri”? Nah Uh – Hey Shrouk! 😉Let me take you on a journey through my experience of building a model using Edge Impulse that responds to my voice saying, “Hey Shrouk!”

Step 1: Collecting Data To kick things off, I needed to gather some data. Edge Impulse made this super easy! I recorded myself saying “Hey Shrouk” multiple times. The interface even had a cool progress ring to show when I was collecting samples. With just a few minutes of audio data, I was all set. 🎤

Step 2: Data Collection Complete Once I had enough samples, Edge Impulse gave me the green light with a big, friendly checkmark. LEVEL UP! ✔️

Step 3: Designing the Impulse Next, I moved on to designing the impulse. Edge Impulse takes raw data, applies signal processing, and uses a learning block to classify new data. It sounds complicated, but let’s walk through it together! 🧙‍♂️

Step 4: Generating Spectrograms To make sense of the audio data, I converted it into spectrograms. This step highlights interesting frequencies and reduces the amount of data, making it easier for the model to understand my voice 🎨

Step 5: Raw Data Visualization Here’s a glimpse of the raw audio data I collected. It’s like looking at the heartbeat of my voice representing every “Hey Shrouk” I recorded! 📈

Step 6: DSP Results The Digital Signal Processing (DSP) results. This step helped the AI model differentiate between my voice and background noise 🔊

Step 7: FFT Bin Weighting Next up was the FFT Bin Weighting. This visual shows how the model processes different frequencies in my voice! 

Step 8: Tuning Feature Parameters I fine-tuned parameters like frame length, frame stride, and filter number. These settings ensure that the model accurately captures the nuances of my voice by changing the size of the sample (i.e. recording!) and how much time we skip forward in the audio recording in each pass!

Step 9: Exploring Features The Feature Explorer gave me a visual representation of all the data features. Seeing the clusters of “Hey Shrouk” data separated from noise was like finding order in chaos! No model is 100% accurate though and we can see a few “Hey Shrouk” outliers that have snuck into the noise and unknown data cluster. 🌌

Step 10: Training the Model Finally, it was time to train the neural network. Edge Impulse showed me the performance metrics, including accuracy, loss, and a confusion matrix. I was excited to see a high accuracy rate of 95.9%! 🚀

Creating a voice-activated assistant with Edge Impulse was an amazing experience! The platform’s user-friendly interface and powerful tools made it easy and fun to bring my project to life. If you’re into AI, machine learning, or just love tinkering with tech, I highly recommend giving Edge Impulse a try. Who knows what awesome projects you’ll come up with next? 😍🧠✨

When I dance, I always dance with a rainbow flag. The same flag that Sarah Hegazi was imprisoned, tortured, and eventually died for. When I dance, I dance as a protest. I dance in all my queerness. I dance in all my Egyptianess. I will not apologise for who I am. I will not apologise for existing.

The Bellydancing Robot: A Blend of Art and Technology

As a neurodivergent, British-Egyptian queer refugee woman in engineering, I am vastly underrepresented in this field. The engineering industry can be incredibly challenging and often feels unwelcoming for people like me. Despite my love for engineering, the lack of diversity and representation makes it a difficult environment. 

Representation is crucial. When people see someone like me succeeding in engineering and combining it with art, it opens doors and inspires others. It challenges the stereotypes and norms that have long dominated this field. It is important to showcase that diversity in engineering leads to innovation and richer, more inclusive technological advancements.

The bellydancing robot project began as a way to explore how robotics could be used in art as a form of resistance against the systemic barriers that exist in my industry. Using my engineering skills and love for dance, I designed a robot that can perform bellydancing moves. It celebrates my cultural heritage.

However, the term “belly dance” is a Western name that has colonised a rich and diverse tradition. The dance I integrate into my robot’s performances is rooted in Egyptian culture, not the often exoticised version seen in Western media. This project is my way of reclaiming and decolonising this art form, highlighting its true origins and cultural significance.

Sarah Hegazi: A Symbol of Courage and Resistance

Sarah Hegazi was an Egyptian LGBTQ+ activist who became a symbol of courage and defiance. In 2017, she was arrested and tortured for raising a rainbow flag at a concert in Cairo. Despite her suffering, Sarah continued to fight for LGBTQ+ rights until her death in 2020. Her story is one of bravery and a strong commitment to justice and equality. Sarah was also my friend.

Combining Technology and Advocacy

The bellydancing robot is a way for me to honour Sarah Hegazi’s legacy. Just as Sarah used her voice to fight for the marginalised, the robot uses dance to promote messages of inclusivity and resilience. By including LGBTQ+ themes in the performances, I aim to raise awareness and create a sense of solidarity within the community.

A Tribute to Sarah Hegazi

Sarah died for my right to wave this flag. I will exercise this right every single chance I get. It’s been almost four years since Sarah died, and at times, it still doesn’t feel real. I met Sarah in 2016 through a mutual friend. We were part of a secret LGBT+ Egyptian group. Sarah always encouraged me to visit Egypt again, saying it was getting safer for LGBTQ+ people.

Then, one night in 2017, at a Mashrou3 Leila concert in Cairo, she waved a rainbow flag. For a few hours, I thought she was right—Egypt was getting safer. But soon, terror swept through our community. People disappeared, and Sarah was taken by the Egyptian secret services.

We later found out she was held in solitary confinement under terrorism charges. She was tortured with electric shocks, beaten, and sexually assaulted. When she was released, activists helped her escape to Canada. Despite the trauma, Sarah continued to support our community.

Why Vital Capacities Is So Important

Vital Capacities is crucial because it merges accessibility with artistic innovation. It provides a unique platform where I can grow and contribute. The residency’s commitment to breaking down barriers and fostering diverse artistic expressions aligns perfectly with my goals to decolonise technology and make engineering more inclusive.

Integrating AI into the Bellydancing Robot

My residency with Vital Capacities is about enhancing my ongoing project by integrating Artificial Intelligence into the bellydancing robot. The aim is to create a dynamic installation that not only performs Egyptian dance but interacts with audiences through AI. It will recognise and respond to a mix of ‘doom’ and ‘tak,’ the core sounds of Egyptian percussion, and choreograph dance movements in real-time.

Integrating AI into my work with the bellydancing robot will be transformative. It will elevate my artistic practice from static performances to interactive, responsive shows where each audience interaction is unique. This technology not only broadens the scope of my performances but also deepens my engagement with audiences, creating a truly immersive experience.

Forever

Sarah’s life and activism have greatly influenced my work. Through the bellydancing robot, I continue her legacy by creating a platform that entertains, educates, and inspires. Each performance is a reminder of the courage needed to stand up against injustice and a tribute to those who fight for equality and human rights.

Next time you wave a rainbow flag, please remember that Sarah Hegazi died for your right to do so. Wear that flag as a badge of honour. Pride is a protest. Our mere existence is an act of defiance. Forever, in your name, Sarah. 🖤✊🏽🏳️‍🌈