1,450 research outputs found

    Using Generic Summarization to Improve Music Information Retrieval Tasks

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    In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most important information for the tasks may not be in those processed segments. In this paper, we leverage generic summarization algorithms, previously applied to text and speech summarization, to summarize items in music datasets. These algorithms build summaries, that are both concise and diverse, by selecting appropriate segments from the input signal which makes them good candidates to summarize music as well. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the performance obtained using summarized datasets against the performances obtained using continuous segments (which is the traditional method used for addressing the previously mentioned time constraints) and full songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based Centrality model improve classification performance when compared to selected 30-second baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin

    Contributions to artificial intelligence: the IIIA perspective

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    La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years

    Mining Entity Synonyms with Efficient Neural Set Generation

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    Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio

    Understanding musical genre preference evolution within a social network

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    Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, specialization in Knowledge Management and Business IntelligenceA música é um campo que simplesmente não pode ser desassociado dos aspetos sociais da vida. Durante a história da humanidade, a música mais popular consistiu sempre num reflexo dos diferentes aspetos da sociedade. Como tal, diferentes estudos foram feitos anteriormente que demonstram este reflexo e obtiveram diversas conclusões. Nesta tese, iremos contribuir para este campo através de uma análise da evolução das preferências de géneros musicais ao longo do tempo através de uma rede social. Usando dados obtidos através de uma experiência de evolução social com cerca de 80 participantes faremos uma análise dos dados existentes. De seguida, esta análise é tida em conta para definir os princípios necessários para representar e analisar a rede social existente. Após esta definição, iremos avaliar a homogeneização da rede social ao longo do tempo. Isto é, iremos avaliar a evolução das diferenças de preferências musicais entre indivíduos que estão ligados na rede social, de forma a perceber se existe alguma tendência de estas diminuírem ao longo do tempo. Um Sequential Algorithm, conhecido como Hidden Markov Model, é aplicado para prever mudanças nas preferências de géneros musicais, considerando as próprias preferências de cada individuo, bem como as preferências dos indivíduos com que este se encontra ligado na nossa rede social. O algoritmo Support Vector Machines é também utilizado para fazer o mesmo tipo de previsão que o modelo anterior servindo como comparação. Por último, discutimos o processo e as limitações que conduziram à definição final do nosso modelo e de forma a contextualizar os resultados que foram obtidos através deste. Em suma, esta tese procurar acrescentar ao trabalho existente em termos de preferências de géneros musicais através de uma avaliação destes dentro do contexto de uma rede social e tendo também em conta a evolução destas ao longo do tempo.Music is a field that simply cannot be disassociated with the social aspects of life. Throughout human history, popular music has always been a reflection of the different aspects of society. As such, there is an interesting amount of studies available that showcase this reflection and draw multiple types of insights. In this thesis, we will look to contribute to this field by assessing the evolution of musical genre preferences over time throughout a social network. Using data obtained through a social evolution experiment of around 80 different individuals we will make an initial assessment of our existing data. This evaluation is then taken into consideration in the next phase of our work where we define the principles necessary to represent and analyse the existing social network. Afterwards, we will showcase a representation of this network, as well as analyse it using various metrics and sub-structures commonly applied in Social Network Analysis. After this, we will evaluate the homogenisation of a network as time goes on. In other words, we will assess the evolution of differences in preferences between individuals that were connected in the social network, in order to understand if there is a trend of these differences diminishing over time. A Sequential-Based algorithm, more specifically, a Hidden Markov Model is used to predict the change in musical genre preferences. This was done by considering each individual’s own preferences as well as the preferences of his connections within the social network with the ultimate goal of assessing how influential the network is in the evolution of a person’s musical genre preferences. To tackle the same research question and provide an alternative approach, as well as a comparison model, we used a Support Vector Machine model. Finally, we discuss the results and limitations that led to our model definition. Overall, this thesis seeks to build upon previous work regarding musical genre preferences by assessing these within the context of a network and taking into account the evolution of these over time

    Does Chatter Matter? The Impact of User-Generated Content on Music Sales

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    The Internet has enabled the era of user-generated content, potentially breaking the hegemony of traditional content generators as the primary sources of “legitimate” information. Prime examples of user-generated content are blogs and social networking sites, which allow easy publishing of and access to information. In this study, we examine the usefulness of such content, consisting of data from blogs and social networking sites in predicting sales in the music industry. We track the changes in online chatter for a sample of 108 albums for four weeks before and after their release dates. We use linear and nonlinear regression to identify the relative significance of online variables on their observation date in predicting future album unit sales two weeks ahead Our findings are as follows: (a) the volume of blog posts about an album is positively correlated with future sales, (b) greater increases in an artist’s Myspace friends week over week have a weaker correlation to higher future sales, (c) traditional factors are still relevant – albums released by major labels and albums with a number of reviews from mainstream sources like Rolling Stone also tended to have higher future sales. More generally, the study provides some preliminary answers for marketing managers interested in assessing the relative importance of the burgeoning number of “Web 2.0” information metrics that are becoming available on the Internet, and how looking at interactions among them could provide predictive value beyond viewing them in isolation. The study also provides a framework for thinking about when user-generated content influences decision making
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