On Tuesday, 8 July 2025 at 18:11:27 UTC, Matthew wrote:
Hi,
I'm writing a program where I'm trying to decode DTMF tones. I
already completed the wave file decoder and I know I'm supposed
to use an FFT to transform the samples from time domain to
frequency domain but I'm stuck on determining which of the DTMF
frequencies are present.
Consider the following, the input is the sound wave samples as
an array of floats between -1 and +1.
```D
void decode_sound(float[] samples)
{
import std.numeric;
import std.math;
import std.complex;
import std.algorithm;
size_t fft_size = 4096;
auto f = new Fft(fft_size);
foreach(ch; samples.chunks(fft_size))
{
auto res = f.fft(ch);
// res is an array of 4096 complex numbers
}
}
```
I can't figure out how the 4096 results of the FFT relate to
the frequencies in the input.
I tried taking the magnitude of each element, I tried taking
just the real or imaginary parts. I plotted them but the graph
doesn't look how I'm expecting.
What do the 4096 resulting complex numbers represent?
How should I use the result to check whether the 1209Hz,
1336Hz, 1477Hz, or 1633Hz tones are present in that part of the
sound?
Thanks,
Matthew
I have not been doing DSP stuff since [2018]. I am not sure how
DTMF is similar to a regular sound signal, but I remember a few
things from sound signals. The FFT result is symmetrical, so you
probably only need to take the first 2048 of 4096. A Spectrogram
would be useful to visualize the components [2]. If this is not
the issue, sorry and please ignore this.
1: https://en.wikipedia.org/wiki/Spectrogram
2:
https://medium.com/@adler7210/manually-decoding-dtmf-through-spectrogram-562e4b0b99c3
2018: Kurtulmuş, F., Öztüfekçi, S., & Kavdır, İ. (2018).
Classification of chestnuts according to moisture levels using
impact sound analysis and machine learning. Journal of Food
Measurement and Characterization, 12(4), 2819-2834.
