Mejoras en el reconocimiento de música manuscrita mediante la re-interpretación de modelos de lenguaje para notación mensural

[ES] El Reconocimiento de Música Manuscrita es la rama del Reconocimiento Óptico de Símbolos dedicada al estudio de la capacidad en los ordenadores para leer notación musical escrita. Esta tecnología busca entender la notación musical para transcribir los trabajos manuscritos a un formato adaptado a ordenador, para que esta música esté disponible al público. Esta tarea ha sido de gran interés útimamente, conforme las tecnologías mejoran y pueden obtener mejores resulltados en este problema. Recientes acercamientos mediante machine learning (aprendizaje máquina) basados en Redes Neuronales Profundas y Recurrentes ya muestran como estos trabajan notablemente mejor en el campo que otros acercamientos tradicionales, especialmente cuando hablamos de Notación Mensural. Estas investigaciones basadas en machine learning han tratado la tarea de reconocer Notación Mensural como otra tarea más de reconocimiento de texto, pero no han explorado las características de los elementos musicales en profundidad. Otros trabajos han tratado de profundizar en el análisis de los elementos musicales y la extracción de sus características, pero a nivel de símbolos segmentados, sin reflejar esto en un entorno de reconocimiento completo o con un conjunto de datos establecido. En este trabajo vamos a tratar de hacer un sistema de reconocimiento completo directamente desde las partituras, utilizando técnicas que ensalzan la información obtenida de los símbolos. Exploramos las interpretaciones del lenguaje para mejorar los resultados en un conjunto de datos disponible públicamente. En nuestros experimentos hemos hecho una mejora del 32% en referencia al error a nivel de símbolo. Con esto, hemos ido de un 5.11% de ratio de error, con las tecnologías de los últimos acercamientos, a un 3.48% de ratio de error, calculado utilizando re-interpretaciones del lenguaje.[EN] Handwritten Music Recognition is the branch of Optical Symbol Recognition dedicated to the study of the capability of computers to read written musical notation. This technology aims to understand musical notation to transcribe the handwritten works into a computer-adapted format, to make this music available to the public. This task has been of great interest lately, as the technologies improve and can get better and better results on this problem. Recent machine learning approaches based on Recurrent and Deep Neural Networks have already shown how these work significantly better in the field than traditional approaches, especially when we are talking about Mensural Notation. These machine learning researches have taken on the task of recognizing Mensural Notation as another written text recognition task, but have not explored the characteristics of musical elements in depth. Other works have tried to dig deeper into analyzing musical elements and the extraction of their characteristics, but at a segmented symbol level, without reflecting this in a complete recognition environment or with an established dataset. In this paper, we will try to make a complete recognition system directly from the scores, using techniques that enhance information obtained from symbols. We explore language interpretations for improving results on a publicly available dataset. In our experiments, we have made a 32\% improvement in regards to error at the symbol level. With this, we have gone from a 5.11\% error rate, using the same technology as the latest approaches, to a 3.48\% error rate, as calculated using language reinterpretations.[CA] El reconeixement de música manuscrita estudia tècniques perquè els ordinadors siguen capaços de transcriure notació musical manuscrita que es troba registrada en imatges a format electrònic, i fer esta música disponible al públic. Recents acostaments d’intel·ligència de màquina basats en Xarxes Neuronals Recurrents i Profundes han mostrat que funcionen significativament millor en aquest problema que l’acostament tradicional basat en models ocults de Markov, especialment en el cas de Notació Mensural. Aquestes investigacions basades en Xarxes Neuronals han investigat la tasca de reconéixer Notació Mensural com una altra tasca de reconeixement de text , però no han explotat les característiques dels elements musicals en profunditat. Altres treballs han intentat aprofundir a analitzar elements musicals i en l’extracció de les característiques des de símbols segmentats, sense reflectir això de manera holística. En aquest treball tractarem de fer un sistema de reconeixement complet directament des dels pentagrames, utilitzant tècniques que enalteixen la informació obtinguda a partir dels símbols. Explorem altres interpretacions de model de llenguatge i provem la nostra proposta en un conjunt de dades disponible de manera pública. En el nostre experiments hem fet una millora del 31% en referència a l’error a nivell de símbol. Amb això, hem anat d’un 3,91% de ràtio d’error, usant tecnologies basades en Xarxes Neuronals, a un 2,7% de ràtio d’error, usant re-interpretacions del model de llenguatge.Villarreal Ruiz, M. (2020). Mejoras en el reconocimiento de música manuscrita mediante la re-interpretación de modelos de lenguaje para notación mensural. Universitat Politècnica de València. http://hdl.handle.net/10251/149020TFG

Aprendizaje semi-supervisado e interactivo para la anotación de un corpus de música histórica manuscrita

[ES] El reconocimiento de música manuscrita es la tarea en la que se emplean tecnologías de la computación para, a partir de una imagen de un pentagrama musical, obtener una transcripción. Si combinamos esto con técnicas de aprendizaje semi supervisado, que permiten etiquetar grandes conjuntos de información a partir de una pequeña parte de los mismos, y aprendizaje interactivo, que permite a un supervisor humano colaborar con la máquina en el proceso de transcripción, lo que obtenemos es un sistema que a partir de unas pocas transcripciones de música puede conseguir una gran base de datos de mucha calidad. Esto es importante debido a que las bases de datos de música manuscrita etiquetadas escasean, aunque la cantidad de obras que es de interés preservar se cuentan en el orden de los millones, tarea inabordable para las personas, por lo que se hace necesario un método que nos permita hacer que todas estas obras sin etiquetar sean etiquetadas con el menor esfuerzo posible. En este trabajo utilizamos tecnologías punteras del reconocimiento de texto manuscrito aplicadas a la música, como son las redes neuronales, tanto convolucionales como recurrentes, y modelos de lenguaje para explorar distintos métodos que faciliten el etiquetado de estos conjuntos de datos. Utilizamos medidas como la probabilidad a posteriori y la entropía de las muestras para determinar como debe distribuirse el esfuerzo humano a la hora de etiquetar muestras manualmente, y mostramos diferentes métodos que determinan si una muestra etiquetada es o no apta para incluirse en el conjunto de datos logrando finalmente un método eficaz para anotar grandes cantidades de muestras con un esfuerzo considerablemente menor.[EN] Handwritten music recognition is the task where computation technologies are used for, from an image of a musical score, obtaining a transcription. If we combine this with semi supervised learning techniques, that allow labeling big information sets from a small fragment of them, and interactive learning, that allow a human supervisor collaborating with the machine in the transcription process, we obtain a system that from a few music transcriptions can accomplish a big data set of great quality. This is important given that labeled handwritten music data sets are scarce, even though the amount of pieces that is of interest to preserve are counted in the order of millions, an unapproachable task for people. This makes necessary a method that allows us to label this pieces with the least effort possible. In this work we use the latest technologies for handwritten text recognition applied to music, such as neural networks, both convolutional and recurrent, and language models to explore different methods that make labeling easier for this data sets. We use measures such as posterior probability and sample entropy to determine how should human effort be used when labeling samples manually, and we show different methods to determine if a sample is or isn't good enough to be included in the data set, accomplishing in the end an effective method to label big amounts of samples with a considerably lesser effort.Villarreal Ruiz, M. (2021). Aprendizaje semi-supervisado e interactivo para la anotación de un corpus de música histórica manuscrita. Universitat Politècnica de València. http://hdl.handle.net/10251/172794TFG

Climatología y reconstrucción de series temporales de descarga fluvial en el Noroeste de Iberia: influencia en el balance de densidad sobre la plataforma

River runoff off northwest Iberia generates a low-density buoyant structure with a strong influence on shelf and coastal circulation. This study estimates the runoff to the shelf of the ten largest rivers in the region based on the furthest downstream gauge records available, and also takes into account the basin area downstream from the station (22% of the basin area for the entire study region). Monthly statistics were computed to obtain mean values for each river to cover the recurrent lack of runoff data in the region. In order to reconstruct gaps in the time series on a daily scale, a method based on the observed discharge of a nearby river basin was used. In addition, the influence of runoff on the shelf was analyzed using monthly CTD data sampled during a 12-year period in the Ría de Vigo and the adjacent shelf. The CTD series shows the existence of a buoyant structure with maximum growth during winter and with large variability of its thermal anomaly. The salinity anomaly correlated significantly with mean winter monthly values of the North Atlantic Oscillation (NAO) index. This atmospheric index integrates both the influence of precipitation —and therefore runoff— and the predominant winds during winter that contribute to the accumulation of fresh water over the shelf.La descarga fluvial en el Noroeste de la Península Ibérica genera una estructura de baja densidad con altas implicaciones en la circulación costera y de plataforma. Este estudio estima la descarga fluvial en la plataforma para los 10 ríos más caudalosos en la región, utilizando para ello los registros de caudal disponibles y suplementados para tener en cuenta el área de la cuenca que se encuentra aguas abajo de las estaciones de aforo (~22% del área total de la región de estudio). Se han calculado valores medios mensuales para cada río, que resultan útiles para cubrir la recurrente carencia de datos en la región de estudio. Para reconstruir huecos en las series temporales en una escala diaria, se utiliza un simple método basado en las observaciones realizadas en una cuenca cercana. La influencia de la descarga fluvial sobre la hidrología en la plataforma es analizada mediante datos mensuales de CTD muestreados en la Ría de Vigo y la plataforma adyacente durante los últimos 12 años. Las series temporales de CTD muestran plumas de agua dulce con máximo crecimiento durante el invierno y con gran variabilidad en su estructura térmica. La correlación de la anomalía de densidad con valores medio invernales del índice de la Oscilación del Atlántico Norte (NAO) muestra valores significativos. Este patrón atmosférico es representativo de la influencia de la precipitación —y por tanto, la descarga fluvial— y los vientos predominantes durante el invierno, que contribuyen a la acumulación de agua dulce sobre la plataforma

NEW CLIMATE SERVICES TO COASTAL COMMUNITIES IN GALICIA (NW SPAIN)

Adaptation to climate change requires the implementation of services that translate scientific knowledge into practical results, so that policymakers and stakeholders can understand the risks and increase their resilience. In the coast those risks are related to flooding, erosion or physico-chemical changes of seawater. The main aim of MarRisk project was to generate this type of services for the NW of the Iberian Peninsula, relying on the experience of a coastal oceanographic observatory (RAIA). These services have been developed based on indicators and models, through a process of co-creation. Thus, a resilience index for harbours or estimations of physical-chemical changes of seawater that can impact sectors such as fishing or aquaculture have been generated. Moreover we have calculated maps of vulnerable areas to flood and erosion. As a conclusion we highlight that the elaboration of a set of indicators together with the expertise of modelization of climate change is not enough to help coastal communities to adapt to climate change. The interaction with different stakeholders is also a needful step to create climate services.En prens

Pseudo-nitzschia australis blooms are not always toxic

Postprin

Harmonisation and dissemination of TSG data from IEO research vessels

Advances in the harmonisation and dissemination of underway data from research vessels in the Spanish Institute of Oceanography (IEO) fleets will be presented.Peer Reviewe

A biogeochemical model for North and Northwest Iberia: some applications

The Coastal and Ocean modeling group at the Spanish Institute of Oceanography (IEO) has a broad experience in hydrodynamic modeling with ROMS in the area of West and North Iberia. Our main task consists of providing insight on the coastal and ocean dynamics in support to the intense IEO ecosystem and fisheries research in the area. The NW coast of Iberia is characterized by high levels of primary production that result from relatively frequent and intense inputs of nutrients caused by upwelling, especially in spring and summer. Primary production sustains wealthy fisheries and aquaculture industries, which constitute a prime economic activity in the region. As a first approach to understand the ecosystem variability in the area we focused on the spring bloom. A high resolution (~3 km) configuration of the ROMS physical model with atmospheric forcing coming from the regional agency Meteogalicia (http://www.meteogalicia.es), which has shown to represent the main features of the shelf and slope circulation in the area, was run coupled to the Fasham-type Fennel biogeochemical model (N2PZD2). Any biogeochemical model aimed at providing a reliable representation of the dynamics of a certain area should be tuned according to its characteristics. In an upwelling system, the composition of phytoplankton varies from the beginning to the end of the bloom. When nutrients and irradiance are high, diatoms are the dominant group, whereas flagellates become more important when upwelling relaxes and, consequently, nutrients and light intensity decrease. In the NW Iberian coast, it has been found that Chaetoceros socialis is the dominant diatom species during the spring bloom (Bode et al, 1996, 1998). For this reason, we have decided to use parameters that are characteristic of plankton at the spring bloom. In particular, the parameters of Chaetoceros socialis have been considered for the unique phytoplankton class of the model. We will show comparisons of the model results for 2006 and 2007 with observations at weekly and daily time scales (MODIS chlorophyll-a images, in situ observations from the “Instituto Español de Oceanografía” Pelacus cruises). The spring bloom is reasonably reproduced in the NW and N coasts in time, space and intensity. The variability between the primary production in 2006 and 2007 can be related to the oceanographic conditions thanks to the use of a numerical model. The results are promising and encourage us to move forward to increase the complexity of our models and broaden their range of application. We will show some examples of the use of the IEO models to get some insight on sardine recruitment variability and harmful algal bloom prediction

Dynamics of river plumes in the South Brazilian Bight and South Brazil

Research articleThe plumes from the rivers of the South Brazilian Bight (SBB) and South Brazil (SB) were studied using a realistic model configuration. River plume variability on continental shelves is driven by the input of river runoff into the shelf, by wind variability, and also by ambient currents and its seasonal variability, especially the Brazil Current, which are realistically modelled in this study. It is presented a simulation of 4 years using a nested configuration, which allows resolving the region around Florianópolis with very high resolution (∼150 m). The dispersion of river plumes was assessed not only with the hydrodynamical model results but also by using passive tracers whose dynamics was analyzed seasonally. Several dyes were released together with the river discharges. This approach allowed calculating the depths of the riverine freshwater, and the resulting regions affected by the plumes. Northward intrusions of waters from the southern region, under the potential influence of the distant La Plata river plume, were evaluated with a Lagrangian approach. The local river plumes are confined to the inner shelf, except south of 30°S where discharges from Lagoa dos Patos disperse over the shelf in the spring and summer. The Brazil Current flowing southward over the slope prevents the river plumes from interaction with oceanic mesoscale dynamics. The river plumes are, thus, mainly controlled by the wind forcing. The plumes from SBB are able to disperse until SB following the southward wind regime typical of the summer. And both the SB and La Plata river plumes are also able to reach SBB, forced by the northward wind typical of the winter season, until the latitude of 25.5°S. A low salinity belt (below 35) is present along the coastal region of SB and SBB year-round, supported by contributions from both the large and small rivers. The interaction between the different plumes influences the dispersion patterns, shielding the Florianṕolis coastal region from plumes of distant rivers, and dispersing the plume of SBB rivers away from Santa Catarina Island as it disperses southward during the summer months.Versión del edito

Automatization of Harmful Algal Bloom early warning services: an example in Galicia (NW Spain)

Predicting Risk and Impact of Harmful Events in the Aquaculture SectorPRIMROS