Cross-Lingual Voice Conversion with Non-Parallel Data

In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context

Efficient Supervised Training of Audio Transformers for Music Representation Learning

In this work, we address music representation learning using convolution-free transformers. We build on top of existing spectrogram-based audio transformers such as AST and train our models on a supervised task using patchout training similar to PaSST. In contrast to previous works, we study how specific design decisions affect downstream music tagging tasks instead of focusing on the training task. We assess the impact of initializing the models with different pre-trained weights, using various input audio segment lengths, using learned representations from different blocks and tokens of the transformer for downstream tasks, and applying patchout at inference to speed up feature extraction. We find that 1) initializing the model from ImageNet or AudioSet weights and using longer input segments are beneficial both for the training and downstream tasks, 2) the best representations for the considered downstream tasks are located in the middle blocks of the transformer, and 3) using patchout at inference allows faster processing than our convolutional baselines while maintaining superior performance. The resulting models, MAEST, are publicly available and obtain the best performance among open models in music tagging tasks.Comment: Accepted at the 2023 International Society for Music Information Retrieval Conference (ISMIR'23

Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification

Prototype Generation (PG) methods are typically considered for improving the efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel $k$NN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving -- both in terms of efficiency and classification performance -- the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting a statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works

Multilabel Prototype Generation for data reduction in K-Nearest Neighbour classification

Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.This research was partially funded by the Spanish Ministerio de Ciencia e Innovación through the MultiScore (PID2020-118447RA-I00) and DOREMI (TED2021-132103A-I00) projects. The first author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”

Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity

In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies show that contrastive learning can be used with editorial metadata (e.g., artist or album name) to learn audio representations that are useful for different classification tasks. In this paper, we extend this idea to using playlist data as a source of music similarity information and investigate three approaches to generate anchor and positive track pairs. We evaluate these approaches by fine-tuning the pre-trained models for music multi-label classification tasks (genre, mood, and instrument tagging) and music similarity. We find that creating anchor and positive track pairs by relying on co-occurrences in playlists provides better music similarity and competitive classification results compared to choosing tracks from the same artist as in previous works. Additionally, our best pre-training approach based on playlists provides superior classification performance for most datasets.Comment: Accepted at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23

Definition and characterization of a historical building by using digital photogrammetry and operational modal analysis : San Juan de los Caballeros Church (Cádiz, Spain)

Congreso celebrado en la Escuela de Arquitectura de la Universidad de Sevilla desde el 24 hasta el 26 de junio de 2015.Nowadays, the preservation of the architectural heritage is a fundamental aspect in the cultural development of modern cities. This heritage has to be preserved and different technical analysis are usually necessary to ensure its proper preservation. The main problem is that the greatest difficulty for the analytical analysis of kind of buildings is the high level of uncertainty associated with many factors. For example, slight modifications of the geometry or the mechanical properties of the structural materials can be the cause of great differences between the results obtained from an analytical analysis and others estimated experimentally. Due to this fact, before performing these analysis, non-destructive techniques are usually an indispensable tool to provide information about the current geometry and the structural behaviour of the building. Thus, the use of photogrammetric techniques and ambient vibration tests allows the right definition of the current geometry and the dynamic characterization of the building, respectively

Modelos para el cálculo de consumo y emisiones gaseosas de la flota de autobuses de Madrid

En este trabajo se presentan algunos resultados del estudio experimental de cálculo de emisiones emanadas en condiciones concretas de la explotación del servicio de la flota de vehículos del transporte público de pasajeros de la ciudad de Madrid, realizado en el marco de cooperación entre grupos de investigación del Instituto Universitario de Investigación del Automóvil (INSIA) de la Universidad Politécnica de Madrid y del Instituto de Desarrollo Industrial (IIDISA) de la Universidad Nacional de Salta. La experiencia consistió en la adquisición de datos de emisiones de contaminantes y consumo de combustible mediante un equipo de medida de emisiones a bordo en un vehículo de prueba en condiciones reales de explotación, en los que se tomaron además, datos de variables cinemáticas en distintas líneas y recorridos representativos de las que conforman el servicio de la Empresa Municipal de Transportes de Madrid. Con los datos adquiridos se han ajustado modelos para la estimación de emisiones contaminantes y consumo en un escenario de 30 ciclos, con el objetivo de obtener valores por unidad (30 ciclos) que pueden ser utilizados como valores de referencia. Por razones de extensión, en este estudio se presentan los resultados del modelo estadístico para el cálculo del consumo y las emisiones totales de CO2 ajustado en función de variables cinemáticas como la velocidad media del autobús y el tiempo, en una de las líneas de servicio más largas de la ciudad: la línea Circular Uno (C1). Las expresiones obtenidas permiten estimar el consumo de combustibles y emisiones de CO2 con valores del coeficiente de correlación superior al 70%. A su vez, es posible realizar un análisis de la gestión de la flota de transporte inspirada en la comparación de los ciclos de operación de las líneas y la evaluación de los impactos producidos por la sustitución de vehículos y combustible

Attitudes and perceptions of medical students about family medicine in Spain: protocol for a cross-sectional survey

Background: Despite the fact that family medicine (FM) has become established as a specialty in the past 25 years, this has not been reflected in the inclusion of the specialty in the majority of medical schools in Spain. Almost 40% of the students will work in primary care but, in spite of this, most universities do not have an assessed placement as such. There are only specific practice periods in health centres or some student-selected components with little weight in the overall curricula. Objectives: To evaluate the attitudes and perceptions of medical students about FM in the health system and their perception about the need for specific training in FM at the undergraduate level. To explore change over time of these attitudes and perceptions and to examine potential predictive factors for change. Finally, we will review what teaching activity in FM is offered across the Spanish schools of medicine. Methods: Descriptive cross-sectional survey. Each one of the different analyses will consist of two surveys: one for all the students in the first, third and fifth year of medical school in all the Spanish schools of medicine asking about their knowledge, perceptions and attitudes in relation to primary care and FM. There will be an additional survey for the coordinating faculty of the study in each university about the educational activities related to FM that are carried out in their centres. The repetition of the study every 2 years will allow for an analysis of the evolution of the cohort of students until they receive their degree and the potential predictive factors. Discussion: This study will provide useful information for strategic planning decisions, content and educational methodology in medical schools in Spain and elsewhere. It will also help to evaluate the influence of the ongoing changes in FM, locally and at the European level, on the attitudes and perceptions of the students towards FM in SpainThis project is funded with a grant from the Instituto de Salud Carlos III, Ministerio de Sanidad, Spain (PI070975). PA-C is funded by a Miguel Servet contract by the Instituto de Salud Carlos III (CP09/00137)

La participación del alumnado en el uso de feedback formativo para la mejora de su aprendizaje

La función de la evaluación formativa es orientar el aprendizaje del alumnado y ofrecer al docente claves para reorientar su enseñanza mientras esta tiene lugar. La orientación del aprendizaje se sustenta en la calidad del feedback recibido y en el uso que se hace de él. Sin embargo, el feedback formativo no suele ser objeto de planificación, y se obvia el seguimiento del uso que hace el alumnado para la autorregulación del aprendizaje. Este trabajo recoge una experiencia innovadora en 7 asignaturas de 3 titulaciones, implicando a 242 estudiantes y 6 docentes. Tiene por objetivos explorar alternativas para la planificación previa y el seguimiento del feedback; aplicar procedimientos para ofrecer feedback que implique a los estudiantes en la autorregulación del aprendizaje; y conocer las percepciones que tienen los estudiantes acerca de la calidad del feedback recibido. Para la valoración de la experiencia se ha empleado un instrumento de planificación y seguimiento del feedback (diario), y un cuestionario dirigido a conocer la percepción del alumnado sobre el feedback recibido. El diario ha permitido la planificación del feedback y su comparación con el aportado. El alumnado señala que en la emisión de feedback ha participado tanto el profesorado como el propio alumnado, que ha recibido feedback después de la entrega del primer borrador y tras la entrega de los trabajos, que éste tuvo que ver prioritariamente con los contenidos de la tarea, y le permitió identificar aspectos a mejorar de la tarea e inferir claves para aplicar en futuras tareas.The function of the assessment is to guide the learning of the students and to offer the teacher keys to reorient their teaching while it takes place. The orientation of learning is based on the quality of the feedback received and on the use made of it. However, the formative feedback is not usually the object of planning, and the follow-up of the use made by the students for the self-regulation of learning is ignored. This paper gathers an innovative experience in 7 subjects of 3 degrees, involving 242 students and 6 teachers. Its objectives are to explore alternatives for prior planning and monitoring of feedback; apply procedures to offer feedback that involves students in the self-regulation of learning; and know the perceptions that students have about the quality of feedback received. For the evaluation of the experience, an instrument for planning and monitoring the feedback (daily) was used, and a questionnaire aimed at understanding the students’ perception of the feedback received. Teacher diaries have allowed the planning of the feedback and its comparison with the one provided. The students pointed out that both the faculty and the students themselves have participated in the broadcast of feedback, which has received feedback after the delivery of the first draft and after the delivery of the work, which had to do primarily with the contents of the task, and allowed them to identify aspects to improve the task and infer keys to apply in future task

Cancer incidence estimation from mortality data: a validation study within a population-based cancer registry

Background: Population-based cancer registries are required to calculate cancer incidence in a geographical area, and several methods have been developed to obtain estimations of cancer incidence in areas not covered by a cancer registry. However, an extended analysis of those methods in order to confirm their validity is still needed. Methods: We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio (IMR), the IMR method. Using the previous 15-year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004– 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004–2013 in Granada. A goodness-of-fit (GOF) indicator was formulated to determine the best estimation scenario. Results: A total of 39,848 cancer incidence cases and 43,884 deaths due to cancer were included. The relative differences between the observed and predicted numbers of cancer cases were less than 10% for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women. The linear assumption was better for lung and ovarian cancers in women and prostate cancer in men. In the best scenario, the mean absolute percentage error was 6% in men and 4% in women for overall cancer. Female breast cancer and prostate cancer obtained the worst GOF results in all scenarios. Conclusion: A comparison with a historical time series of real data in a population-based cancer registry indicated that the IMR method is a valid tool for the estimation of cancer incidence. The goodness-of-fit indicator proposed can help select the best assumption for the IMR based on a statistical argument.Subprogram "Cancer surveillance" of the CIBER of Epidemiology and Public Health (CIBERESP)MINECO/FEDER PGC2018-098860-B-I00Andalusian Department of Health Research, Development and Innovation PI-0152/201