@topherbuckley Here is a sample code which plots the spectrogram with the acoustics package and with scipy.signal.spectrogram().

import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as ss
import acoustics

N_POINTS = 200000
MAIN_FREQUENCY = 500
SAMPLING_FREQUENCY = 5000

FFT_POINTS = 4096
FFT_OVERLAP = 128
PLOT_CLIM = (40, 100)

P_REF = 2e-5

plt.ion()

T = np.arange(N_POINTS)/SAMPLING_FREQUENCY
Y_main = np.sin(2*np.pi*MAIN_FREQUENCY*T)
Y = Y_main + np.random.rand(N_POINTS)
Y = ss.detrend(Y)

rms = np.sqrt(np.mean(Y_main**2))
rms_dB = 20*np.log10(rms/P_REF)
print('Amplitude of main frequency = {:.2f} dB'.format(rms_dB))

# Plot spectrogram using acoustics
signal = acoustics.Signal(Y, SAMPLING_FREQUENCY)
signal.plot_spectrogram(title='acoustics.Signal.plot_spectrogram()')

# Spectrogram with scipy
f, t, Sxx = ss.spectrogram(Y, fs=SAMPLING_FREQUENCY,
                           window='hanning',
                           nperseg=FFT_POINTS,
                           noverlap=FFT_OVERLAP,
                           mode='psd',
                           scaling='spectrum')
Sxx_dB = 10*np.log10(Sxx/P_REF**2)
print('Max amplitude using Scipy = {:.2f} dB'.format(Sxx_dB.max()))

fig = plt.figure()
pc = plt.pcolormesh(t, f, Sxx_dB)
pc.set_clim(PLOT_CLIM)
cb = fig.colorbar(pc)
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.title('scipy.signal.spectrogram()')
cb.set_label('SPL [dB re. 20 $\mathrm{\mu{}Pa}$]')

plot_spectrogram() doesn't show dB re. 2e-5 Pa #211

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