3/31/2023 0 Comments Scilab fft example![]() ![]() Second, we can’t have negative magnitude values in our noise spectrum. This step is straightforward once you understand the following two things: First, we’re working only with FFT magnitudes here, not the complex numbers that are the initial result of the FFT computation. We’re treating the spectrum more like a normal array of data points. I’m not going to convert the horizontal axis to frequency because we’re not really thinking in terms of real-life frequencies at this point. ![]() The following plots show the general shape of my noise spectrum. Using an averaged noise spectrum did create a difference in the final recording, though I wouldn’t say that the overall quality was better. I then computed the magnitude spectrum for each one and averaged them all to create my final noise spectrum. So I split the 60-second recording into six 10-second segments. > Noise_FFTMag(1,k) = (NoiseAudio_1_FFTMag(k) NoiseAudio_2_FFTMag(k) NoiseAudio_3_FFTMag(k) NoiseAudio_4_FFTMag(k) NoiseAudio_5_FFTMag(k) NoiseAudio_6_FFTMag(k))/6 NoiseAudio_6_FFTMag = abs(fft(NoiseAudio_6)) NoiseAudio_2_FFTMag = abs(fft(NoiseAudio_2)) NoiseAudio_1_FFTMag = abs(fft(NoiseAudio_1)) = wavread("C:\Users\Robert\Documents\Audio\Noise.wav") My voice recording is about 10 seconds long, so why did I record 60 seconds of noise? Well, this was my attempt to incorporate averaging into the noise spectrum: As you will see later, you want the noise recording to be the same length as the voice recording. I duplicated the audio setup used to create the original audio file, then I captured about 60 seconds of “silence.” This silence becomes hiss in the recording. It’s possible that a more sophisticated implementation would produce better results, and perhaps I’ll write another article on spectrum subtraction if I find a way to make it more effective. However, the reduction in noise volume was, for some reason, almost unnoticeable. It preserved the quality of the voice better than filtering did, and FFT plots confirm that it can remove narrowband noise components in a way that filtering cannot. If you’re a bit confused, don’t worry-these steps will be discussed in greater detail as we work our way through the Scilab commands.Īlso, I’m going to warn you ahead of time that spectral subtraction didn’t work very well for me. Create the reduced-noise time-domain waveform via the inverse FFT.Insert phase information into the reduced-noise spectrum by duplicating the phase information from the FFT of the original recording.Generate a reduced-noise magnitude spectrum of the audio file by subtracting the noise spectrum from the magnitude of the FFT of the original recording.(Additional steps, namely averaging and prewhitening, can be performed to make the noise spectrum a more effective means of reducing noise.).Generate a noise spectrum by computing the FFT and saving the magnitude data.Make a recording that contains only background noise (or extract a noise-only portion of the original recording).It’s called spectrum subtraction, and it consists of the following steps: In this article we’ll look at a different technique for audio noise reduction. Another concern is that the filtering action is not selective: it does not distinguish between signal and noise within a given frequency band, and consequently the characteristics of the voice are altered in undesirable ways. One problem is that you can’t suppress noise frequencies that are close to signal frequencies, especially if your system cannot realistically implement a very-high-order filter. ![]() In the previous article, we used a filter to suppress noise components in a voice recording.
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