Abstract
We present Essentia 2.0, an open-source C++ library for
audio analysis and audio-based music information retrieval
released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio
input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large
set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a
number of predefined executable extractors for the available
music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly.
Furthermore, it includes a Vamp plugin to be used with
Sonic Visualiser for visualization purposes. The library is
cross-platform and currently supports Linux, Mac OS X,
and Windows systems. Essentia is designed with a focus on
the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms.
The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is
easily expandable and allows for both research experiments
and development of large-scale industrial applications.
audio analysis and audio-based music information retrieval
released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio
input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large
set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a
number of predefined executable extractors for the available
music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly.
Furthermore, it includes a Vamp plugin to be used with
Sonic Visualiser for visualization purposes. The library is
cross-platform and currently supports Linux, Mac OS X,
and Windows systems. Essentia is designed with a focus on
the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms.
The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is
easily expandable and allows for both research experiments
and development of large-scale industrial applications.
| Original language | English |
|---|---|
| Pages | 18-21 |
| Number of pages | 4 |
| Volume | 6 |
| No. | 1 |
| Specialist publication | ACM SIGMM Records |
| Publication status | Published - Mar 2014 |
| Externally published | Yes |