Harvesting and Structuring Social Data in Music Information Retrieval

Abstract. An exponentially growing amount of music and sound resources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted information we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology

Flabase: towards the creation of a flamenco music knowledge base

Online information about flamenco music is scattered overdifferent sites and knowledge bases. Unfortunately, thereis no common repository that indexes all these data. Inthis work, information related to flamenco music is gath-ered from general knowledge bases (e.g., Wikipedia, DB-pedia), music encyclopedias (e.g., MusicBrainz), and spe-cialized flamenco websites, and is then integrated into anew knowledge base called FlaBase. As resources fromdifferent data sources do not share common identifiers, aprocess of pair-wise entity resolution has been performed.FlaBase contains information about 1,174 artists, 76pa-los(flamenco genres), 2,913 albums, 14,078 tracks, and771 Andalusian locations. It is freely available in RDF andJSON formats. In addition, a method for entity recognitionand disambiguation for FlaBase has been created. The sys-tem can recognize and disambiguate FlaBase entity refer-ences in Spanish texts with an f-measure value of 0.77. Weapplied it to biographical texts present in Flabase. By usingthe extracted information, the knowledge base is populatedwith relevant information and a semantic graph is createdconnecting the entities of FlaBase. Artists relevance is thencomputed over the graph and evaluated according to a fla-menco expert criteria. Accuracy of results shows a highdegree of quality and completeness of the knowledge base

Contrastive Learning for Cross-modal Artist Retrieval

Music retrieval and recommendation applications often rely on content features encoded as embeddings, which provide vector representations of items in a music dataset. Numerous complementary embeddings can be derived from processing items originally represented in several modalities, e.g., audio signals, user interaction data, or editorial data. However, data of any given modality might not be available for all items in any music dataset. In this work, we propose a method based on contrastive learning to combine embeddings from multiple modalities and explore the impact of the presence or absence of embeddings from diverse modalities in an artist similarity task. Experiments on two datasets suggest that our contrastive method outperforms single-modality embeddings and baseline algorithms for combining modalities, both in terms of artist retrieval accuracy and coverage. Improvements with respect to other methods are particularly significant for less popular query artists. We demonstrate our method successfully combines complementary information from diverse modalities, and is more robust to missing modality data (i.e., it better handles the retrieval of artists with different modality embeddings than the query artist's)

Bootstrapping a Music Voice Assistant with Weak Supervision

One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries. This can be particularly challenging when entities are in the tenth of millions, as is the case of e.g. music catalogs. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited. This paper presents a weakly-supervised methodology to label large amounts of voice query logs, enhanced with a manual filtering step. Our experimental evaluations show that slot tagging models trained on weakly-supervised data outperform models trained on hand-annotated or synthetic data, at a lower cost. Further, manual filtering of weakly-supervised data leads to a very significant reduction in Sentence Error Rate, while allowing us to drastically reduce human curation efforts from weeks to hours, with respect to hand-annotation of queries. The method is applied to successfully bootstrap a slot tagging system for a major music streaming service that currently serves several tens of thousands of daily voice queries

Supervised and Unsupervised Learning of Audio Representations for Music Understanding

In this work, we provide a broad comparative analysis of strategies for pre-training audio understanding models for several tasks in the music domain, including labelling of genre, era, origin, mood, instrumentation, key, pitch, vocal characteristics, tempo and sonority. Specifically, we explore how the domain of pre-training datasets (music or generic audio) and the pre-training methodology (supervised or unsupervised) affects the adequacy of the resulting audio embeddings for downstream tasks. We show that models trained via supervised learning on large-scale expert-annotated music datasets achieve state-of-the-art performance in a wide range of music labelling tasks, each with novel content and vocabularies. This can be done in an efficient manner with models containing less than 100 million parameters that require no fine-tuning or reparameterization for downstream tasks, making this approach practical for industry-scale audio catalogs. Within the class of unsupervised learning strategies, we show that the domain of the training dataset can significantly impact the performance of representations learned by the model. We find that restricting the domain of the pre-training dataset to music allows for training with smaller batch sizes while achieving state-of-the-art in unsupervised learning -- and in some cases, supervised learning -- for music understanding. We also corroborate that, while achieving state-of-the-art performance on many tasks, supervised learning can cause models to specialize to the supervised information provided, somewhat compromising a model's generality

Automatic Detection of Melodic Patterns in Flamenco Singing by Analyzing Polyphonic Music Recordings

In this work an analysis of characteristic melodic pattern in flamenco fandango style is carried out. Contrary to other analysis, where corpora are searched for characteristic melodic patterns, in this work characteristic melodic patterns are defined by flamenco experts and then searched in the corpora. In our case, the corpora were composed of pieces taken from two fandango styles, Valverde fandangos and Huelva capital fandangos. The chosen styles are representative of fandango styles and are also different as for their musical characteristics. The patterns provided by the flamenco experts were specified in MIDI format, but the corpora under study were provided in audio format. Two algorithms had to be designed to accomplish the goal of our research: first, an algorithm extracting audio features from the corpus and outputting a MIDI-like format; second, an algorithm to actually perform the search based on the output provided by the first algorithm. Flamenco experts assessed the results of the searches and drew conclusions

Valores funcionales respiratorios pre y post-operatorios en pacientes sometidos a resección pulmonar.

Se realizó un estudio observacional, analítico, prospectivo, en el período 2006-2008, en el Hospital Neumológico Benéfico Jurídico, con el objetivo de comparar los valores predictivos espirométricos de los enfermos candidatos a resección pulmonar con los obtenidos después de la intervención quirúrgica. La muestra quedó formada por 28 pacientes, residentes en Ciudad de La Habana. Para comparar el valor predictivo del volumen espiratorio forzado en el primer segundo con el valor real, seis meses después de la resección pulmonar se calculó el cociente delta relativo que expresó en porcentaje el grado de coincidencia entre estas dos variables. Una vez recogida la información se sometió a un análisis exploratorio de datos, con pruebas de significación asociadas. Los resultados mostraron que la espirometría clínica tiene un espacio indiscutible en la evaluación de la función pulmonar preoperatoria de los candidatos a resección pulmonar. Los valores predictivos y reales después de la resección pulmonar del volumen espiratorio forzado en el primer segundo fueron semejantes. La lobectomía superior izquierda fue el tipo de intervención quirúrgica con mayor similitud entre estos valores, por el contrario la neumectomía izquierda resultó ser la técnica con mayores diferencias entre los mismos.  Palabras clave: Resección pulmonar, pruebas funcionales ventilatorias, espirometría

Phenylisoxazole-3/5-Carbaldehyde Isonicotinylhydrazone Derivatives: Synthesis, Characterization, and Antitubercular Activity

14 pags, 5 figs, 3 tabs, 1 schEight new phenylisoxazole isoniazid derivatives, 3-(2′-fluorophenyl)isoxazole-5-carbaldehyde isonicotinylhydrazone (1), 3-(2′-methoxyphenyl)isoxazole-5-carbaldehyde isonicotinylhydrazone (2), 3-(2′-chlorophenyl)isoxazole-5-carbaldehyde isonicotinylhydrazone (3), 3-(3′-clorophenyl)isoxazole-5-carbaldehyde isonicotinylhydrazone (4), 3-(4′-bromophenyl)isoxazole-5-carbaldehyde isonicotinylhydrazone (5), 5-(4′-methoxiphenyl)isoxazole-3-carbaldehyde isonicotinylhydrazone (6), 5-(4′-methylphenyl)isoxazole-3-carbaldehyde isonicotinylhydrazone (7), and 5-(4′-clorophenyl)isoxazole-3-carbaldehyde isonicotinylhydrazone (8), have been synthesized and characterized by FT-IR, 1H-NMR, 13C-NMR, and mass spectral data. The 2D NMR (1H-1H NOESY) analysis of 1 and 2 confirmed that these compounds in acetone-d6 are in the trans(E) isomeric form. This evidence is supported by computational calculations which were performed for compounds 1-8, using DFT/B3LYP level with the 6-311++G(d,p) basis set. The in vitro antituberculous activity of all the synthesized compounds was determined against the Mycobacterium tuberculosis standard strains: sensitive H37Rv (ATCC-27294) and resistant TB DM97. All the compounds exhibited moderate bioactivity (MIC = 0.34-0.41 μM) with respect to the isoniazid drug (MIC = 0.91 μM) against the H37Rv sensitive strain. Compounds 6 (X = 4′-OCH3) and 7 (X = 4′-CH3) with MIC values of 12.41 and 13.06 μM, respectively, were about two times more cytotoxic, compared with isoniazid, against the resistant strain TB DM97.W. H. and F. C. acknowledge Universidad de Lima Scientific Research Institute for the financial support to carry out this research work. E. S. thanks Financiamiento Basal para Centros Cientificos y Tecnologicos de Excelencia, AFB10008. J. Z. D. thanks Consejo Superior de Investigacion Cientifica (CSIC, Spain). S. O. thanks Ministerio de Ciencias, Innovacion y Universidades (MICINN (RTI2018-094356-B-C21)) and Cabildo de Tenerife (Agust ' in de Betancourt Program).Peer reviewe

Knowledge extraction and representation learning for music recommendation and classification

In this thesis, we address the problems of classifying and recommending music present in large collections. We focus on the semantic enrichment of descriptions associated to musical items (e.g., artists biographies, album reviews, metadata), and the exploitation of multimodal data (e.g., text, audio, images). To this end, we first focus on the problem of linking music-related texts with online knowledge repositories and on the automated construction of music knowledge bases. Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation. Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification, combining audio, text, and images. We show how the semantic enrichment of texts and the combination of learned data representations improve the performance on both tasks.En esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados.Programa de doctorat en Tecnologies de la Informació i les Comunicacion

FLABASE: A Flamenco Knowledge Base

Data was compiled and curated from different sources: Wikipedia, DBpedia, Andalucia.org, elartedevivirelflamenco.com, MusicBrainz, flun.cica.es/index.php/grabaciones/base-datos-grabaciones and juntadeandalucia.es/institutodeestadisticaycartografia/simaFlaBase (Flamenco Knowledge Base) is the acronym of a new knowledge base of flamenco music. Its ultimate aim is to gather all available online editorial, biographical and musicological information related to flamenco music. A first version is just being released. Its content is the result of the curation and extraction processes. FlaBase is stored in JSON format, and it is freely available for download. This first release of FlaBase contains information about 1,102 artists, 74 palos (flamenco genres), 2,860 albums, 13,311 tracks, and 771 Andalusian locations