Detect periodic vocalizations with RIBBIT
This module provides functionality to search audio for periodically fluctuating vocalizations.
calculate_pulse_score(amplitude, amplitude_sample_rate, pulse_rate_range, plot=False, nfft=1024)¶
Search for amplitude pulsing in an audio signal in a range of pulse repetition rates (PRR)
scores an audio amplitude signal by highest value of power spectral density in the PRR range
- amplitude – a time series of the audio signal’s amplitude (for instance a smoothed raw audio signal)
- amplitude_sample_rate – sample rate in Hz of amplitude signal, normally ~20-200 Hz
- pulse_rate_range – [min, max] values for amplitude modulation in Hz
- plot=False – if True, creates a plot visualizing the power spectral density
- nfft=1024 – controls the resolution of the power spectral density (see scipy.signal.welch)
pulse rate score for this audio segment (float)
pulse_finder_species_set(spec, species_df, window_len='from_df', plot=False)¶
perform windowed pulse finding (ribbit) on one file for each species in a set
- spec – opensoundscape.Spectrogram object
- species_df –
a dataframe describing species by their pulsed calls. columns: species | pulse_rate_low (Hz)| pulse_rate_high (Hz) | low_f (Hz)| high_f (Hz)| reject_low (Hz)| reject_high (Hz) |window_length (sec) (optional) | reject_low2 (opt) | reject_high2 |
- window_len – length of analysis window, in seconds. Or ‘from_df’ (default): read from dataframe. or ‘dynamic’: adjust window size based on pulse_rate
the same dataframe with a “score” (max score) column and “time_of_score” column
ribbit(spectrogram, signal_band, pulse_rate_range, window_len, noise_bands=None, plot=False)¶
Run RIBBIT detector to search for periodic calls in audio
This tool searches for periodic energy fluctuations at specific repetition rates and frequencies.
- spectrogram – opensoundscape.Spectrogram object of an audio file
- signal_band – [min, max] frequency range of the target species, in Hz
- pulse_rate_range – [min,max] pulses per second for the target species
- windo_len – the length of audio (in seconds) to analyze at one time - one RIBBIT score is produced for each window
- noise_bands – list of frequency bands to subtract from the desired signal_band For instance: [ [min1,max1] , [min2,max2] ] - if None, no noise bands are used - default: None
- plot=False – if True, plot the power spectral density for each window
pulse score (float) for each time window array of time: start time of each window
array of pulse_score
__PARAMETERS__ RIBBIT requires the user to select a set of parameters that describe the target vocalization. Here is some detailed advice on how to use these parameters.
Signal Band: The signal band is the frequency range where RIBBIT looks for the target species. It is best to pick a narrow signal band if possible, so that the model focuses on a specific part of the spectrogram and has less potential to include erronious sounds.
Noise Bands: Optionally, users can specify other frequency ranges called noise bands. Sounds in the noise_bands are _subtracted_ from the signal_band. Noise bands help the model filter out erronious sounds from the recordings, which could include confusion species, background noise, and popping/clicking of the microphone due to rain, wind, or digital errors. It’s usually good to include one noise band for very low frequencies – this specifically eliminates popping and clicking from being registered as a vocalization. It’s also good to specify noise bands that target confusion species. Another approach is to specify two narrow noise_bands that are directly above and below the signal_band.
Pulse Rate Range: This parameters specifies the minimum and maximum pulse rate (the number of pulses per second, also known as pulse repetition rate) RIBBIT should look for to find the focal species. For example, choosing pulse_rate_range = [10, 20] means that RIBBIT should look for pulses no slower than 10 pulses per second and no faster than 20 pulses per second.
Window Length: This parameter tells RIBBIT how many seconds of audio to analyze at one time. Generally, you should choose a window_length that is similar to the length of the target species vocalization, or a little bit longer. For very slowly pulsing vocalizations, choose a longer window so that at least 5 pulses can occur in one window (0.5 pulses per second -> 10 second window). Typical values for window_length are 1 to 10 seconds.
Plot: We can choose to show the power spectrum of pulse repetition rate for each window by setting plot=True. The default is not to show these plots (plot=False).
__ALGORITHM__ This is the procedure RIBBIT follows: divide the audio into segments of length window_len for each clip:calculate time series of energy in signal band (signal_band) and subtract noise band energies (noise_bands) calculate power spectral density of the amplitude time series score the file based on the maximum value of power-spectral-density in the pulse rate range
summarize_top_scores(audio_files, list_of_result_dfs, scale_factor=1.0)¶
find the highest score for each file and each species, and put them in a dataframe
Note: this function expects that the first column of the results_df contains species names
- audio_files – a list of file paths
- list_of_result_dfs – a list of pandas DataFrames generated by ribbit_species_set()
- scale_factor=1.0 – optionally multiply all output values by a constant value
a dataframe summarizing the highest score for each species in each file