I'm happy to announce the release of scaper v0.2.0.

Scaper is a python library for soundscape synthesis and augmentation. It can be used to quickly and easily generate audio data for machine learning experiments with audio, for example for sound event detection in environmental and bioacoustics audio or (with some work) audio source separation as well!

The latest release includes improvements to how scaper computes the loudness of foreground and background events, resulting in more reliable SNR in the output audio.

Installing the latest release is as easy as calling: >pip install scaper

If you haven't used scaper before, you might want to check out the scaper documentation  and in particular the scaper tutorial.

And, if you really want to dig into the why/what/how behind scaper, please see the scaper-paper:

Scaper: A Library for Soundscape Synthesis and Augmentation
J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello.
In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017.
[PDF]

Happy synthesis!

Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.

For example, we used the Noise Profiler to rapidly explore and visualize noise patterns in NYC during weekdays versus weekends across multiple locations, using time series data from SONYC noise sensors:

For further details see our paper:

Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
F. Miranda, M. Lage, H. Doraiswamy, C. Mydlarz, J. Salamon, Y. Lockerman, J. Freire, C. Silva
Computer Graphics Forum (EuroVis '18), 37(3), 2018, 13-22
[Wiley][PDF][BibTeX]