In [1]:

```
%matplotlib inline
import numpy, scipy, matplotlib.pyplot as plt, IPython.display as ipd
import librosa, librosa.display
import stanford_mir; stanford_mir.init()
```

Musical signals are highly non-stationary, i.e., their statistics change over time. It would be rather meaningless to compute a single Fourier transform over an entire 10-minute song.

The **short-time Fourier transform (STFT)** (Wikipedia; FMP, p. 53) is obtained by computing the Fourier transform for successive frames in a signal.

$$ X(m, \omega) = \sum_n x(n) w(n-m) e^{-j \omega n} $$

As we increase $m$, we slide the window function $w$ to the right. For the resulting frame, $x(n) w(n-m)$, we compute the Fourier transform. Therefore, the STFT $X$ is a function of both time, $m$, and frequency, $\omega$.

Let's load a file:

In [2]:

```
x, sr = librosa.load('audio/brahms_hungarian_dance_5.mp3')
ipd.Audio(x, rate=sr)
```

Out[2]: