How I made an App that Translates Cat Sounds to Human Language
I believe that to translate a cat’s sound, you need to distinguish if it’s actually a cat’s sound in the first place. Makes sense, doesn’t it? To achieve this, I initially considered designing a sophisticated deep learning algorithm capable of recognizing various sounds. However, I opted for a simpler route and utilized Huawei Technologies’ ML Kit Sound Detector instead. This tool can detect up to 12 different sounds, including cat sounds.
Alright, we’ve managed to distinguish between a meow and a fart sound — progress! Now, I needed a collection of cat sound examples that represent specific cat behaviors. To achieve this, I followed the work of Yagya Raj Pandeya and Joonwhoan Lee on Domestic Cat Sound Classification Using Transfer Learning. Fortunately, cats steer clear of political discussions. When they vocalize, it’s usually about particular situations or behaviors:
- Hunger
- Eagerness to play/hunt/go out
- Annoyance/danger
- Sleepiness
- Contentment/happiness
- Amorous feelings
- Anger
Having categorized these behaviors, it’s time to gather some examples. Luckily, many people record their cats while they meow. The downside? My YouTube history is now filled with searches like:
“Horny cat sounds- 1 Hour HIGH QUALITY”
For those wondering, this is what a horny cat sound graph looks like
Once I gathered all the raw example data in MP3 format, it was time to compare these sounds to those made by Gilbert. It’s important to note that this app is tailored specifically for Gilbert, so all the data used pertains to one-year-old male cats. I’m not entirely sure what you’ll do with this information, but there it is. Now, I needed to compare the sound recorded from the app with the example data and determine which Cat Sound class it belongs to.
We now possess the raw audio recordings from Gilbert along with our pre-trained examples, marking the moment to compare the audio spectrums. This comparison will determine which of the seven behaviors Gilbert’s sounds align with. To accomplish this, I utilized an Analyzer that is derived from Google’s Audio Analyzer.
Once I identified the type of meow, I displayed a random quote from the behavior list. For instance, if the behavior is “Hungry” and it’s before 12 pm, the app might generate a quote like “I’m hungry, give me my breakfast!”