How to determine the frequency range of interested sound with ambient noise

I'm very new to signal processing. I have two sound signal data right now. Each of the data is collected at a sample rate of 10 KHz, 2 seconds. I have imported this data into python. Both sound_1 and sound_2 is a numpy array right now. The length of each sound data is 20000 of course.

Sound_1 contains a water flow sound(which I'm interested) and environmental noise(I'm not interested), while sound_2 only contains environment noise(I'm not interested).

I'm looking for an algorithm(or package) which can help me determine the frequency range of this water flow sound. I think if I can find out the frequency range, I can use an inverse Fourier transform to filter the environment noise.

However, my ultimate purpose is to extract the water flow sound from sound_1 data and eliminate environmental noise. It would be great if there are other approaches.

I'm currently looking at this post: Python frequency detection

But I don't understand how they can find out the frequency by only one sound signal. I think we need to compare 2 signal data at least(one contains the sound I am interested, the other doesn't), so we can find out the difference.

Solution

Since sound_1 contains both water flow and environmental noise, there's no straightforward way of extracting the water flow. The Fourier transform will get you all frequencies in the signal, irrespective of the source.

The way to approach is get frequencies of environmental noise from sound_2 and then remove them from sound_1. After that is done, you can extract the frequencies from already denoised sound_1.

One of popular approaches to such noise reduction is with spectral gating. Essentially, you first determine how the noise sounds like and then remove smoothed spectrum from your signal. Smoothing is crucial, as sound is a wave, a continuous entity. If you simply chop out discrete frequencies from the wave, you will get very poor results (audio will sound unnatural and robotic). The amount of smoothing you apply will determine how much noise is reduced (mind it's never truly removed - you will always get some residue).

To the concrete solution.

As you're new to the subject, I'd recommend first how noise reduction works in a software that will do the work for you. Audacity is an excellent choice. I linked the manual for noise reduction, but there are plenty of tutorials out there.
After you know what you want to get, you can either implement spectral gating yourself or use existing package. Audacity has an excellent implementation in C++, but it may prove difficult to a newbie to port. I'd recommend going first with noisereduce package. It's based on Audacity implementation. If you use it, you will be done in a few lines.

Here's a snippet:

import noisereduce as nr
# load data
rate, data = wavfile.read("sound_1.wav")
# select section of data that is noise
noisy_part = wavfile.read("sound_2.wav")
# perform noise reduction
reduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noisy_part, verbose=True)

Now simply run FFT on the reduced_noise to discover the frequencies of water flow.

Here's how I am using noisereduce. In this part I am determining the frequency statistics.