Dmitry Bogdanov
José Zapata
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.
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Digital Library
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- F. Fuhrmann, P. Herrera, and X. Serra. Detecting solo phrases in music using spectral and pitch-related descriptors. Journal of New Music Research, 38(4):343--356, 2009.Google ScholarCross Ref
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- S. Koelsch, S. Skouras, T. Fritz, P. Herrera, C. Bonhage, M. Kuessner, and A. M. Jacobs. Neural correlates of music-evoked fear and joy: The roles of auditory cortex and superficial amygdala. Neuroimage. In press.Google Scholar
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- J. Serrà, E. Gómez, P. Herrera, and X. Serra. Chroma binary similarity and local alignment applied to cover song identification. IEEE Trans. on Audio, Speech, and Language Process., 16(6):1138--1151, 2008. Google ScholarDigital Library
- M. Sordo. Semantic Annotation of Music Collections: A Computational Approach. PhD thesis, UPF, Barcelona, Spain, 2012.Google Scholar
- N. Wack, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serra, E. Gomez, and P. Herrera. Music classification using high-level models. In Music Inf. Retrieval Evaluation Exchange (MIREX'10), 2010.Google Scholar
Index Terms
- ESSENTIA: an open-source library for sound and music analysis
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