GETTING STARTED

scikit-maad是一个用于声景分析的信号处理库，有以下四种功能
(1) load and process digital audio
(2) segment and find regions of interest
(3) compute acoustic features
(4) estimate sound pressure level

安装
pip install scikit-maad
导入
import maad

DOCUMENTATION

Sound processing

Input and output
load
load_url
load_spectrogram
write
Preprocess audio
fir_filter
sinc
smooth
select_bandwidth
pcen
remove_background
remove_background_morpho
remove_background_along_axis
median_equalizer
wave2frames
Transform audio
spectrogram
avg_power_spectro
avg_amplitude_spectro
linear_to_octave
envelope
spectrum
resample
trim
normalize
Metrics
temporal_snr
spectral_snr
sharpness

Segmentation methods

Temporal
find_rois_cwt
Spectro-temporal
create_mask
select_rois
rois_to_imblobs

Acoustic features

Sound pressure level

Utilities

EXAMPLE

References
[1] Sifre, L., & Mallat, S. (2013). Rotation, scaling and deformation invariant scattering for texture discrimination. Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference On, 1233–1240. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6619007

[2] Lee, D., & Sueng, S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791. https://doi.org/10.1038/44565

[3] Towsey, M., Znidersic, E., Broken-Brow, J., Indraswari, K., Watson, D. M., Phillips, Y., Truskinger, A., & Roe, P. (2018). Long-duration, false-colour spectrograms for detecting species in large audio data-sets. Journal of Ecoacoustics, 2(1), 1–1. https://doi.org/10.22261/JEA.IUSWUI
Ulloa, J. S., Aubin, T., Llusia, D., Bouveyron, C., & Sueur, J. (2018). Estimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis. Ecological Indicators, 90, 346–355. https://doi.org/10.1016/j.ecolind.2018.03.026

[4] Maaten, L. van der, & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579–2605.

[5] Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 96(34), 226–231.

[6] Towsey, M., 2013b. Noise Removal from Wave-forms and Spectrograms Derived from Natural Recordings of the Environment. Queensland University of Technology, Brisbane

[7] Sueur, J., Farina, A., Gasc, A., Pieretti, N., & Pavoine, S. (2014). Acoustic Indices for Biodiversity Assessment and Landscape Investigation. Acta Acustica United with Acustica, 100(4), 772–781. https://doi.org/10.3813/AAA.918757

[8] Buxton, R. T., McKenna, M. F., Clapp, M., Meyer, E., Stabenau, E., Angeloni, L. M., Crooks, K., & Wittemyer, G. (2018). Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity: Acoustical Monitoring. Conservation Biology, 32(5), 1174–1184. https://doi.org/10.1111/cobi.13119

[9] Towsey, M., Wimmer, J., Williamson, I., & Roe, P. (2014). The use of acoustic indices to determine avian species richness in audio-recordings of the environment. Ecological Informatics, 21, 110–119. https://doi.org/10.1016/j.ecoinf.2013.11.007