Audio visualization is a popular technique used to represent audio data in a visual form. One of the commonly used methods for audio visualization is the Fast Fourier Transform (FFT). In this blog post, we will explore how to implement audio visualization using FFT in C++.

What is FFT?

The Fast Fourier Transform (FFT) is an algorithm that computes the Discrete Fourier Transform (DFT) of a sequence of samples. In simpler terms, it converts a time-domain signal into its frequency-domain representation.

Implementing Audio Visualization using FFT

To implement audio visualization using FFT in C++, we can follow these steps:

1. Read and Decode Audio: Start by reading and decoding the audio file using a library like libsndfile or any other suitable library.

#include <iostream>
#include <sndfile.h>

int main() {
    const char* audioFilePath = "path/to/audio/file.wav";
    SF_INFO fileInfo;
    SNDFILE* audioFile = sf_open(audioFilePath, SFM_READ, &fileInfo);
    if (audioFile == NULL) {
        std::cerr << "Error opening audio file!" << std::endl;
        return -1;
    }

    // Read and decode audio here

    sf_close(audioFile);
    return 0;
}

1. Apply FFT: Once the audio data is decoded, we can proceed to apply the Fast Fourier Transform to convert the audio data from the time-domain to the frequency-domain.

#include <complex>
#include <vector>

std::vector<std::complex<double>> applyFFT(const std::vector<double>& audioData) {
    // Apply FFT here and return the frequency-domain representation
    // Use a library like FFTW or any other suitable library to perform FFT

    std::vector<std::complex<double>> frequencyData;
    // ...

    return frequencyData;
}

int main() {
    // ...

    // Read and decode audio here

    std::vector<double> audioData;
    // ...

    std::vector<std::complex<double>> frequencyData = applyFFT(audioData);

    // ...
}

1. Visualize Data: Once we have the frequency-domain representation of the audio data, we can visualize it using a suitable library like matplotlib or OpenGL. This step involves mapping the frequency data to visual elements like bar charts or spectrograms.

#include <matplotlibcpp.h>

namespace plt = matplotlibcpp;

void visualizeData(const std::vector<std::complex<double>>& frequencyData) {
    // Visualize the frequency data using a suitable library
    // Map the frequency data to visual elements like bar charts or spectrograms
    // Use a library like matplotlib or OpenGL to create visualizations

    plt::plot(/* x-axis data */, /* y-axis data */);
    plt::show();
}

int main() {
    // ...

    // Read and decode audio here

    std::vector<double> audioData;
    // ...

    std::vector<std::complex<double>> frequencyData = applyFFT(audioData);

    visualizeData(frequencyData);

    // ...
}

Conclusion

In this blog post, we have seen how to implement audio visualization using FFT in C++. We first read and decode the audio file, then applied the FFT to convert the audio data to the frequency domain. Finally, we visualized the frequency data using a suitable library. Visualization of audio data provides a captivating and insightful representation of the audio signals, allowing users to better understand the characteristics of the sound.