Recognizing Musical Entities in Usergenerated Content Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it canimprove the performance of several tasks such as music recommendation, genre classification or artist similarity. However, most entity recognition systems in the music domain have concentrated on formal texts (e.g. artistsxe2x80x99 biographies, encyclopedic articles, etc.), ignoring rich and noisy usergenerated content. In this work, we present a novel method to recognize musicalentities in Twitter content generated by users following a classical music radio channel. Our approach takes advantage of both formal radio schedule and usersxe2x80x99 tweets to improve entity recognition. We instantiate several machine learning algorithms to perform entity recognition combining taskspecific and corpusbased features. We also show how to improve recognition results by jointly considering formal and usergenerated content.

Music Symbol Sequence Indexing in Medieval Plainchant Manuscripts Huge amounts of musical manuscripts are preserved in cathedrals, abbeys, and archives. However, without reliable transcripts, their contents are inaccessible. Manual transcription is unaffordable for large collections, and current automatic technologiesxe2x80x94such as Optical Music Recognition or Handwritten Music Recognitionxe2x80x94do not provide sufficient accuracy for a fullyautomatic scenario. In many cases, perfect transcripts are not really needed, given that contentbased search with some degree of reliability would already be extremely useful. Spotting just single music symbols is rather useless (most of the symbols generally appear in all pages); instead, helpful search targets are melodic patterns, which typically correspond to music symbol sequences. We explore approaches for accurate retrieval of melodic patterns, represented by music symbol sequences, from collections of Medieval plainchant manuscripts. Our statistical framework, based on the use of convolutional recurrent neural networks and probabilistic indices, is shown to be useful for retrieving music patterns which appear frequently in this untranscribed images, yielding an Average Precision of 86 %.