maad.sound.spectral_snr

maad.sound.spectral_snr(Sxx_power)[source]

Compute the signal to noise ratio (SNR) of an audio from its spectrogram in the time-frequency domain.

Parameters:

Sxx_power2D array: Power spectrogram density [Matrix] to process.

Returns:

ENRffloat: Total energy in dB computed in the frequency domain which corresponds to the average of the power spectrogram then the sum of the average
BGNffloat: Estimation of the background energy (dB) computed based on the estimation of the noise profile of the power spectrogram (2d)
SNRf: float: Signal to noise ratio (dB) computed in the frequency domain SNRf = ENRf - BGNf
ENRf_per_binvector of floats: Energy in dB per frequency bin
BGNf_per_binvector of floats: Background (noise profile) energy in dB per frequency bin
SNRf_per_binvector of floats: Signal to noise ratio per frequency bin

References

..[1] Towsey, Michael (2013), Noise Removal from Waveforms and Spectrograms Derived from Natural Recordings of the Environment. Queensland University of Technology, Brisbane. ..[2] Towsey, Michael (2017),The calculation of acoustic indices derived from long-duration recordings of the naturalenvironment.Queensland University of Technology, Brisbane.

Examples

>>> s, fs = maad.sound.load('../data/rock_savanna.wav')
>>> Sxx_power,_,_,_ = maad.sound.spectrogram (s, fs)  
>>> _, _, snr, _, _, _ = maad.sound.spectral_snr(Sxx_power)
>>> snr
4.084065436435541