10,463 research outputs found
Software agents in music and sound art research/creative work: Current state and a possible direction
Composers, musicians and computer scientists have begun to use software-based agents to create music and sound art in both linear and non-linear (non-predetermined form and/or content) idioms, with some robust approaches now drawing on various disciplines. This paper surveys recent work: agent technology is first introduced, a theoretical framework for its use in creating music/sound art works put forward, and an overview of common approaches then given. Identifying areas of neglect in recent research, a possible direction for further work is then briefly explored. Finally, a vision for a new hybrid model that integrates non-linear, generative, conversational and affective perspectives on interactivity is proposed
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the Consejería de Innovación y Ciencia de Andalucía
Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies
An automatic word classification system has been designed which processes
word unigram and bigram frequency statistics extracted from a corpus of natural
language utterances. The system implements a binary top-down form of word
clustering which employs an average class mutual information metric. Resulting
classifications are hierarchical, allowing variable class granularity. Words
are represented as structural tags --- unique -bit numbers the most
significant bit-patterns of which incorporate class information. Access to a
structural tag immediately provides access to all classification levels for the
corresponding word. The classification system has successfully revealed some of
the structure of English, from the phonemic to the semantic level. The system
has been compared --- directly and indirectly --- with other recent word
classification systems. Class based interpolated language models have been
constructed to exploit the extra information supplied by the classifications
and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Melomics: A Case-Study of AI in Spain
Traditionally focused on good old-fashioned
AI and robotics, the Spanish AI community
holds a vigorous computational intelligence
substrate. Neuromorphic, evolutionary, or
fuzzylike systems have been developed by many
research groups in the Spanish computer sciences.
It is no surprise, then, that these naturegrounded
efforts start to emerge, enriching the
AI catalogue of research projects and publications
and, eventually, leading to new directions
of basic or applied research. In this article, we
review the contribution of Melomics in computational
creativity.The work on Iamus was partially supported by grants
IPT-300000-2010-010 from the Spanish Ministerio de
Ciencia e Innovación and TSI-090302-2011-8 from
the Spanish Ministerio de Industria, Turismo y Comercio.
The first and fourth authors were supported by
grant P09-TIC-5123 from the Consejería de Innovación
y Ciencia, Junta de Andalucía
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