Utilities¶
The module utils has a handful of useful set of tools used in the audio analysis framework.
Visualization¶
rand_cmap(nlabels[, type, ...]) |
Creates a random colormap to be used together with matplotlib. |
crop_image(im, tn, fn[, fcrop, tcrop]) |
Crop a spectrogram (or an image) in time (horizontal X axis) and frequency (vertical y axis) |
save_figlist(fname, figlist) |
Save a list of figures or spectrograms to disk. |
plot1d(x, y[, ax]) |
Plot the waveform or spectrum of an audio signal. |
plot_wave(s, fs[, tlims, ax]) |
Plot audio waveform. |
plot_spectrum(pxx, f_idx[, ax, flims, ...]) |
Plot power spectral density estimate (PSD). |
plot2d(im[, ax, colorbar]) |
Display the spectrogram of an audio signal. |
plot_spectrogram(Sxx, extent[, db_range, ...]) |
Plot spectrogram represenation. |
overlay_rois(im_ref, rois[, edge_color, ...]) |
Display bounding boxes with time-frequency regions of interest over a spectrogram. |
overlay_centroid(im_ref, centroid[, savefig]) |
Overlay centroids on the original spectrogram |
plot_features_map(df[, norm, mode]) |
Plot features values on a heatmap. |
plot_features(df[, ax, norm, mode]) |
Plot the variation of features values (ie. |
plot_correlation_map(df[, R_threshold, method]) |
Plot the correlation map between indices in the DataFrame obtained with maad. |
plot_shape(shape, params[, row, ax, ...]) |
Plot shape features in a bidimensional plot. |
false_Color_Spectro(df[, indices, plim, ...]) |
Create False Color Spectrogram from indices obtained by MAAD. |
Mathematical¶
running_mean(x, N[, mode]) |
Compute fast running mean for a window size N. |
get_unimode(X[, mode, axis, N, N_bins, verbose]) |
Get the statistical mode or modal value which is the most common number in the dataset. |
entropy(x[, axis]) |
Compute the entropy of a vector (waveform) or matrix (spectrogram). |
rms(s) |
Compute the root-mean-square (RMS) level of an input signal. |
kurtosis(x[, axis]) |
Compute the kurtosis (tailedness or curved or arching) of an audio signal. |
skewness(x[, axis]) |
Compute the skewness (asymetry) of an audio signal. |
moments(X[, axis]) |
Computes the first 4th moments of a vector (1d, ie. |
Parser¶
read_audacity_annot(audacity_filename) |
Read Audacity annotations file (or labeling file) and return a Pandas Dataframe with the bounding box and the label of each region of interest (ROI). |
write_audacity_annot(fname, df_rois[, save_file]) |
Write audio segmentation to text file in Audacity format, a file that can be imported and modified with Audacity. |
read_raven_annot(raven_filename) |
Read Raven annotations file (or labeling file) and return a Pandas Dataframe with the bounding box and the label of each region of interest (ROI). |
write_raven_annot(fname, df_rois[, save_file]) |
Write audio segmentation to text file in Raven format, a file that can be imported and modified with Raven. |
date_parser(datadir[, dateformat, ...]) |
Parse all filenames contained in a directory and its subdirectories. |
Miscellaneous¶
index_bw(fn, bw) |
Select all the index coresponding to a selected frequency band. |
into_bins(x, an, bin_step[, axis, bin_min, ...]) |
Divide a vector (1D) or a matrix (2D) into multiple bins according to a bin_step with respect of the energy |
shift_bit_length(x) |
Find the closest power of 2 that is superior or equal to the number x. |
rle(x) |
Compute the Run-Length encoding (RLE) of a vector. |
linear_scale(x[, minval, maxval, axis]) |
Scale the values of a vector or matrix from a user specified minimum to a user specified maximum. |
amplitude2dB(x[, db_range, db_gain]) |
Transform amplitude data (signal, scalar) into decibel scale within the dB range (db_range). |
power2dB(x[, db_range, db_gain]) |
Transform power (amplitude²) signal or scalar into decibel scale within the dB range (db_range). |
dB2amplitude(x[, db_gain]) |
Transform data in dB scale into amplitude A gain (db_gain) could be added at the end. |
dB2power(x[, db_gain]) |
Transform data in dB scale into power (amplitude²) A gain (db_gain) could be added at the end. |
mean_dB(*argv[, axis]) |
Compute the average of decibel values. |
add_dB(*argv[, axis]) |
Computes an addition on decibel values. |
nearest_idx(array, value) |
Find nearest value on array and return its index. |
get_df_single_row(df, index[, mode]) |
Extract a single row from a dataframe keeping the DataFrame type (instead of becoming a Series). |
format_features(df, tn, fn) |
Format features such as bounding box coordinates and centroids coordinates to predifined format : time-frequency to pixels or pixels to time-frequency units. |
crossfade(s1, s2, fs[, fade_len]) |
Add a smooth transition (cross-fade) between two audio signals. |
crossfade_list(s_list, fs[, fade_len]) |
Apply a cross-fade to a list of audio signals. |
Xeno-Canto¶
xc_query(searchTerms[, max_nb_files, ...]) |
Query metadata from Xeno-Canto website depending on the search terms. |
xc_multi_query(df_query[, max_nb_files, ...]) |
Multi_query performs multiple queries following the search terms defined in the input dataframe |
xc_selection(df_dataset[, max_nb_files, ...]) |
Select a maximum number of recordings depending on their quality and duration in order to create an homogeneous dataset. |
xc_download(df, rootdir[, dataset_name, ...]) |
Download the audio files from Xeno-Canto based on the input dataframe It will create directories for each species if needed |
Audio metadata¶
check_file_format(path_audio) |
Check Wave file consistency. |
audio_header(path_audio) |
Get audio header information from WAVE file. |
filename_info(path_audio[, verbose]) |
Get information from filename when using standard format. |
get_metadata_file(path_audio[, verbose]) |
Get metadata asociated with audio recordings in audio file. |
get_metadata_dir(path_dir[, verbose]) |
Get metadata asociated with audio recordings in a directory. |