1. Link To The Code: https://editor.p5js.org/yl7799/sketches/rAWnxxvTm
2. Process Sketches
Gather Samples:
Train Our Model:
Export Our Model:
3. Final Outcome
No Sound:
Elephant Sounds:
Bird Sounds:
Dog Sounds:
Cow Sounds:
Cat Sounds:
4. Reflection Work
This work aimed to train a classifier to classify animal sounds, which included the calls of elephants, birds, dogs, cows, and cats. First, I visited the Teachable Machines website and used the sounds project to create my model. My classifier model design process went through the following three steps. Collecting samples, training our model, and outputting our model.
In this work, I collected elephants, birds, dogs, cows, and cat sound on youtube as training samples. And I learned the code of the ML5 case, then I used the ML5 library, wrote the code to classify the sound, and loaded the saved model. Eventually, I implemented the classification of calls with animals, and the experimental results proved the effectiveness of the trained model. The results of this experiment also show that the design of my current work is reasonable.
Through this work on designing software classifiers, I learned how to use the ML5 library for machine learning on the web. In addition, I also acquired the skills of creating and exploring artificial intelligence in the browser. For this work, I also studied how to use the toolkit to build a classifier. In the future, I will continue to use ML5 to explore new artificial intelligence doors actively.
5. The Links To My Collection of Animal Sounds On Youtube Are As Follows:
Elephant sounds:
https://www.youtube.com/watch?v=b81v8h5Fy1g
Bird sounds:
https://www.youtube.com/watch?v=Uxuo_MI27jY
Dog sounds:
https://youtu.be/o9WNsU_d87g
Cow sounds:
https://www.youtube.com/watch?v=vq6yPWM64OQ
Cat sounds:
https://www.youtube.com/watch?v=pIi5tvwpnuw