I have a set of audio files rescued from a source of phonograph record like a gramophone. Very old family stuff records from my elders back in year 1946. A relative has digitalized these with a turntable and gave the records to me in lossless WAV Format. The audios are just conversations of my relatives in cotidiane situations. So it is just vocals materials but mixed with a heavy quantity of noise. Noise is mostly concentrated in the mid-high frequencies, like 1Khz-10Khz.
So the records are VERY noisy. SNR is like 0dB or worst. My mission is to filter out the noise.
One advantage is that noise exhibits a cyclical pattern. I tried some classic vinyl restoration stuff like decracking o declicking with Sound Forge, DART 24 Pro. Results were unsuccessful. The best result I had is the "Noise reduction" effect in Audacity, which did output relatively good. But this last one just filters the frequencies where the noise is with narrow FFT. So the vocals content loses the high-mid frequencies. Leaving only mid or mid-bass content. The noise got down but voices are unintelligible.
So I think perhaps I could turn to a Artificial Intelligence solution?. but I have no clue from where can I start. I have some understading of theory of AI, the Perceptron, neural networks, Backpropagation, but I never applied them to audio. Perhaps a Convolution Neural network could be a solution, but how could I train it?. If helps, I have programming skills in C# and knowledge about digital linear filters like IIR and FIR. But equalization based solutions *are not, what I need *.
Below I offer an example track for your consideration.
What can I do for filter this kind of cyclical pattern noise?