Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
Proceedings of the international conference on Multimedia
Ecological acoustics perspective for content-based retrieval of environmental sounds
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on environmental sound synthesis, processing, and retrieval
Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification
IEEE Transactions on Audio, Speech, and Language Processing
Unifying Low-Level and High-Level Music Similarity Measures
IEEE Transactions on Multimedia
Information Processing and Management: an International Journal
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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.