Academic research using Essentia

The list below highlights some of the academic studies using Essentia organized by research topics.

Music analysis datasets

  • Porter, A., Bogdanov D., Kaye R., Tsukanov R., & Serra X. (2015). AcousticBrainz: a community platform for gathering music information obtained from audio. 16th International Society for Music Information Retrieval Conference (ISMIR 2015). 786-792.

Music classification

  • D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, and P. Herrera. Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management, 49(1):13–33, Jan. 2013.
  • N. Wack, E. Guaus, C. Laurier, R. Marxer, D. Bogdanov, J. Serrà, and P. Herrera. Music type groupers (MTG): generic music classification algorithms. In Music Information Retrieval Evaluation Exchange (MIREX’09), 2009.
  • 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 Information Retrieval Evaluation Exchange (MIREX’10), 2010.
  • C. Laurier. Automatic Classification of Musical Mood by Content- Based Analysis. PhD thesis, UPF, Barcelona, Spain, 2011.
  • C. Laurier, O. Meyers, J. Serrà, M. Blech, P. Herrera, and X. Serra. Indexing music by mood: design and integration of an auto- matic content-based annotator. Multimedia Tools and Applications, 48(1):161–184, 2009.

Semantic autotagging

  • M. Sordo. Semantic Annotation of Music Collections: A Computational Approach. PhD thesis, UPF, Barcelona, Spain, 2012.
  • Yang, Y., Bogdanov, D., Herrera, P., & Sordo, M. (2012). Music Retagging Using Label Propagation and Robust Principal Component Analysis. In International World Wide Web Conference (WWW’12). International Workshop on Advances in Music Information Research (AdMIRe’12).

Music similarity and recommendation

  • D. Bogdanov. From music similarity to music recommendation: Com- putational approaches based on audio and metadata analysis. PhD thesis, UPF, Barcelona, Spain, 2013.
  • D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, and P. Herrera. Semantic audio content-based music recommendation and visualization based on user preference examples. Information Pro- cessing & Management, 49(1):13–33, Jan. 2013.
  • D. Bogdanov, J. Serrà, N. Wack, P. Herrera, and X. Serra. Unifying low-level and high-level music similarity measures. IEEE Trans. on Multimedia, 13(4):687–701, 2011.
  • O. Celma, P. Cano, and P. Herrera. Search sounds an audio crawler focused on weblogs. In 7th Int. Conf. on Music Information Retrieval (ISMIR), 2006.

Visualization and interaction with music

  • D. Bogdanov. From music similarity to music recommendation: Computational approaches based on audio and metadata analysis. PhD thesis, UPF, Barcelona, Spain, 2013.
  • D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, and P. Herrera. Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management, 49(1):13–33, Jan. 2013.
  • C. F. Julià and S. Jordà. SongExplorer: a tabletop application for exploring large collections of songs. In Int. Society for Music Information Retrieval Conf. (ISMIR’09), 2009.
  • C. Laurier, M. Sordo, and P. Herrera. Mood cloud 2.0: Music mood browsing based on social networks. In Int. Society for Music Information Retrieval Conf. (ISMIR’09), 2009.
  • Mayor, O., Llop, J., & Maestre, E. (2011). RepoVizz: A multimodal on-line database and browsing tool for music performance research. In International Society for Music Information Retrieval Conference (ISMIR’11).
  • M. Sordo, G. K. Koduri, S. Şentürk, S. Gulati, and X. Serra. A musically aware system for browsing and interacting with audio music collections. In The 2nd CompMusic Workshop, 2012.
  • Augello, A., Infantino, I., Manfrè, A., Pilato, G., Vella, F., & Chella, A. (2016). Creation and cognition for humanoid live dancing. Robotics and Autonomous Systems.

Sound indexing

  • M. Haro, J. Serrà, P. Herrera, and A. Corral. Zipf’s law in short-time timbral codings of speech, music, and environmental sound signals. PLoS ONE, 7(3):e33993, 2012.
  • J. Janer, M. Haro, G. Roma, T. Fujishima, and N. Kojima. Sound object classification for symbolic audio mosaicing: A proof-of-concept. In Sound and Music Computing Conf. (SMC’09), pages 297–302, 2009.
  • G. Roma, J. Janer, S. Kersten, M. Schirosa, P. Herrera, and X. Serra. Ecological acoustics perspective for content-based retrieval of environmental sounds. EURASIP Journal on Audio, Speech, and Music Processing, 2010.

Instrument detection

  • F. Fuhrmann and P. Herrera. Quantifying the relevance of locally extracted information for musical instrument recognition from entire pieces of music. In Int. Society for Music Information Retrieval Conf. (ISMIR’11), 2011.
  • 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.

Cover detection

  • 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 Processing, 16(6):1138–1151, 2008.

Computational musicology

  • G. K. Koduri, S. Gulati, P. Rao, and X. Serra. Raga recognition based on pitch distribution methods. Journal of New Music Research, 41(4):337–350, 2012.
  • G. K. Koduri, J. Serrà, and X. Serra. Characterization of intonation in carnatic music by parametrizing pitch histograms. In Int. Society for Music Information Retrieval Conf. (ISMIR’12), pages 199–204, 2012.

Acoustic analysis for medical and neuroimaging studies

  • Koelsch, S., Skouras, S., Fritz, T., Herrera, P., Bonhage, C., Kuessner, M., & Jacobs, A. M. (2013). Neural correlates of music-evoked fear and joy: The roles of auditory cortex and superficial amygdala. Neuroimage, 81, 49-60.
  • Vaiciukynas, E., Verikas, A., Gelzinis, A., Bacauskiene, M., Vaskevicius, K., Uloza, V., ... & Ciceliene, J. (2016, August). Fusing Various Audio Feature Sets for Detection of Parkinson’s Disease from Sustained Voice and Speech Recordings. In International Conference on Speech and Computer (pp. 328-337). Springer International Publishing.