Code
FS = 22050
BROADBAND = (20, 10000) # the page's broadband noise
def envelope(n, rise, decay):
"""Linear rise and decay ramps, in seconds, over `n` samples."""
i = np.arange(n)
return np.clip(np.minimum(i / max(rise * FS, 1), (n - 1 - i) / max(decay * FS, 1)), 0, 1)
def ramped(snd, ramp=10e-3):
"""`snd` with linear ramps at both ends."""
return so.Sound(envelope(snd.n_samples, ramp, ramp) * snd.data[:, 0], FS)
def tone(freq, dur, level_db=0.0, ramp=10e-3):
"""A sine tone at `level_db` dB re RMS 1, `dur` seconds long including linear ramps."""
return ramped(so.pure_tone(dur, FS, freq, phase=-np.pi / 2), ramp).gain_db(level_db)
def noise(dur, band, level_db, rng, ramp=10e-3):
"""Gaussian noise in `band` (Hz, brick-wall) at an RMS of `level_db` dB re a pure tone's."""
snd = so.gaussian_noise(dur, FS, band=band, rng=rng).gain_db(level_db)
return ramped(snd, ramp)
def spectrum_noise(dur, band, spectrum_db, rng, ramp=10e-3):
"""Gaussian noise in `band` at a spectrum level of `spectrum_db` dB per Hz."""
return noise(dur, band, spectrum_db + 10 * np.log10(band[1] - band[0]), rng, ramp)
def place(events, dur=None):
"""Sounds added at their onset times: `events` is a list of (onset in seconds, Sound)."""
end = max(onset + snd.duration for onset, snd in events)
out = np.zeros((round((dur or end) * FS), 1))
for onset, snd in events:
i = round(onset * FS)
out[i : i + snd.n_samples] += snd.data[: len(out) - i]
return so.Sound(out, FS)
def finish_together(sounds):
"""Sounds scaled by one gain, as `finish` would scale the loudest of them, so their levels
keep their relation from one sound to the next."""
loudest = max(sounds, key=lambda snd: snd.rms)
gain = 0.1 / loudest.rms
gain = min(gain, 0.95 / max(snd.peak for snd in sounds))
return [(snd * gain).ramp(5e-3) for snd in sounds]
def show(snd, fmin=50, fmax=11000, log=True, win=30e-3):
"""A spectrogram, on a log frequency axis unless `log` is False. Returns the figure and the
panels the playhead follows."""
fig, ax = plt.subplots(figsize=(10, 3.2), layout="constrained")
stft = so.STFT(snd, win_dur=win, hop_dur=5e-3)
step = stft.f[1] - stft.f[0]
keep = (stft.f >= fmin - step) & (stft.f <= fmax + step) # one bin past each edge
top = stft.db[:, keep].max()
ax.pcolormesh(stft.t, stft.f[keep], stft.db[0][keep], vmin=top - 70, vmax=top, cmap="magma")
if log:
so.plotting.log_frequency_axis(ax)
ax.set(ylim=(fmin, fmax), ylabel="Frequency [Hz]", title=f"Spectrogram (Hann {win * 1000:g} ms)")
ax.set(xlabel="Time [s]", xlim=(0, snd.duration))
return fig, [ax]
