24,131 research outputs found
Acoustic Scene Classification
This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council
Editorial: Perceptual issues surrounding the electroacoustic listening experience
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link
3D audio as an information-environment: manipulating perceptual significance for differntiation and pre-selection
Contemporary use of sound as artificial information display is rudimentary, with little 'depth of significance' to facilitate users' selective attention. We believe that this is due to conceptual neglect of 'context' or perceptual background information. This paper describes a systematic approach to developing 3D audio information environments that utilise known cognitive characteristics, in order to promote rapidity and ease of use. The key concepts are perceptual space, perceptual significance, ambience labelling information and cartoonification
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The evolution of rhythmic cognition: New perspectives and technologies in comparative research
Music is a pervasive phenomenon in human culture, and musical rhythm is virtually present in all musical traditions. Research on the evolution and cognitive underpinnings of rhythm can benefit from a number of approaches. We outline key concepts and definitions, allowing fine-grained analysis of rhythmic cognition in experimental studies. We advocate comparative animal research as a useful approach to answer questions about human music cognition and review experimental evidence from different species. Finally, we suggest future directions for research on the cognitive basis of rhythm. Apart from research in semi-natural setups, possibly allowed by “drum set for chimpanzees” prototypes presented here for the first time, mathematical modeling and systematic use of circular statistics may allow promising advances
Multimodal Content Analysis for Effective Advertisements on YouTube
The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201
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