I’m working on on this research to classify blueberries based on their texture—specifically, whether they are soft, juicy, or crunchy—using the sounds they produce when crushed.
I have about 1100 audio samples, and I’ve generated spectrograms for each sample. Unfortunately, I don’t have labeled data, so I can’t directly apply supervised machine learning techniques. Instead, I’m looking for effective ways to differentiate between these three categories based on the spectrograms. I’ve attached examples of spectrograms for what I believe might be soft, juicy, and crunchy blueberries. However, since the data isn’t labeled, I’m unsure if these assumptions are correct.

Crunchy Berries: When crushed, they produce separate, distinct peaks in the audio signal. These peaks are spaced out over time, indicating that the berry is breaking apart in a crisp, segmented manner.

crunchyberry

Juicy Berries: When crushed, they generate continuous peaks in the audio signal. These peaks are more closely packed together and sustained, indicating a burst of juice and flesh, with less resistance, creating a smoother sound.

juicyberry

Soft Berries: These produce very few and small peaks. The sound is faint and less defined, indicating that the berry crushes easily with little resistance, creating minimal disruption in the audio signal.

softberry

What I Tried:

I attempted to classify the blueberries by detecting peaks within a specific timeframe of the audio signal. This method allowed me to differentiate between soft and crunchy berries effectively, as soft berries produce fewer and smaller peaks, while crunchy berries have distinct, separated peaks.

What I Expected:

I expected this peak detection approach to also help classify juicy berries, as I anticipated continuous, higher amplitude peaks that would be distinct from the other categories.

What Actually Happened:

While the method worked well for soft and crunchy berries, it did not successfully differentiate the juicy berries. The continuous nature of the juicy berry peaks did not stand out as much as I expected, making it difficult to classify them accurately.

Can anyone help me out with some ideas to solve this problem? If you want we can work on this together and write a research paper or an article in journal.