654 research outputs found
Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators’ Reactions.
Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this paper we study spectators’ responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators’ physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g. special effects, empathy and compassion toward a character, etc.) and (ii) watching a movie together causes spectators’ affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators’ signals, such as pairwise, group and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators’ synchronization and detect aesthetic highlights based on both spectators’ electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection
Semi-supervised spectral clustering with automatic propagation of pairwise constraints
International audienceIn our data driven world, clustering is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on ground truth to perform the classification and are usually subject to overtraining issues. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty of the task, unsupervised learning tends to provide inferior results to supervised learning. To boost their performance, a compromise is to use learning only for some of the ambiguous classes. In this context, this paper studies the impact of pairwise constraints to unsupervised Spectral Clustering. We introduce a new generalization of constraint propagation which maximizes partitioning quality while reducing annotation costs. Experiments show the efficiency of the proposed scheme
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition
Predominant Musical Instrument Classification based on Spectral Features
This work aims to examine one of the cornerstone problems of Musical
Instrument Retrieval (MIR), in particular, instrument classification. IRMAS
(Instrument recognition in Musical Audio Signals) data set is chosen for this
purpose. The data includes musical clips recorded from various sources in the
last century, thus having a wide variety of audio quality. We have presented a
very concise summary of past work in this domain. Having implemented various
supervised learning algorithms for this classification task, SVM classifier has
outperformed the other state-of-the-art models with an accuracy of 79%. We also
implemented Unsupervised techniques out of which Hierarchical Clustering has
performed well.Comment: Appeared in Proceedings of SPIN 202
Video Background Music Generation: Dataset, Method and Evaluation
Music is essential when editing videos, but selecting music manually is
difficult and time-consuming. Thus, we seek to automatically generate
background music tracks given video input. This is a challenging task since it
requires plenty of paired videos and music to learn their correspondence.
Unfortunately, there exist no such datasets. To close this gap, we introduce a
dataset, benchmark model, and evaluation metric for video background music
generation. We introduce SymMV, a video and symbolic music dataset, along with
chord, rhythm, melody, and accompaniment annotations. To the best of our
knowledge, it is the first video-music dataset with high-quality symbolic music
and detailed annotations. We also propose a benchmark video background music
generation framework named V-MusProd, which utilizes music priors of chords,
melody, and accompaniment along with video-music relations of semantic, color,
and motion features. To address the lack of objective metrics for video-music
correspondence, we propose a retrieval-based metric VMCP built upon a powerful
video-music representation learning model. Experiments show that with our
dataset, V-MusProd outperforms the state-of-the-art method in both music
quality and correspondence with videos. We believe our dataset, benchmark
model, and evaluation metric will boost the development of video background
music generation
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