1,450 research outputs found
Using Generic Summarization to Improve Music Information Retrieval Tasks
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
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
Addressing Tempo Estimation Octave Errors in Electronic Music by Incorporating Style Information Extracted From Wikipedia
(Abstract to follow
Mining Entity Synonyms with Efficient Neural Set Generation
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
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
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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