Automatic transfer of musical mood into virtual environments
28 Nov 2018-
TL;DR: A method that automatically transforms a virtual environment according to the mood of input music using machine learning to extract a mood from the music and transfers valence-related aspects, but not arousal-related ones is presented.
Abstract: This paper presents a method that automatically transforms a virtual environment (VE) according to the mood of input music. We use machine learning to extract a mood from the music. We then select images exhibiting the mood and transfer their styles to the textures of objects in the VE photorealistically or artistically. Our user study results indicate that our method is effective in transferring valence-related aspects, but not arousal-related ones. Our method can still provide novel experiences in virtual reality and speed up the production of VEs by automating its procedure.
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
TL;DR: Reports of affective experience obtained using SAM are compared to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974), which requires 18 different ratings.
7,472 citations
27 Jun 2016
TL;DR: A Neural Algorithm of Artistic Style is introduced that can separate and recombine the image content and style of natural images and provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
Abstract: Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
4,888 citations
"Automatic transfer of musical mood ..." refers methods in this paper
...For style transfer, we use the two most popular methods of artistic [11] and photorealistic transfer [19]....
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...Our approach tested in this paper is re-stylizing a VE by transferring texture (representing mood) from an image to the VE using image style transfer [11]....
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21 Oct 2013
TL;DR: OpenSMILE 2.0 as mentioned in this paper unifies feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing, allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries).
Abstract: We present recent developments in the openSMILE feature extraction toolkit. Version 2.0 now unites feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing. Descriptors from audio and video can be processed jointly in a single framework allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries), such as moments, peaks, regression parameters, etc. Postprocessing of the features includes statistical classifiers such as support vector machine models or file export for popular toolkits such as Weka or HTK. Available low-level descriptors include popular speech, music and video features including Mel-frequency and similar cepstral and spectral coefficients, Chroma, CENS, auditory model based loudness, voice quality, local binary pattern, color, and optical flow histograms. Besides, voice activity detection, pitch tracking and face detection are supported. openSMILE is implemented in C++, using standard open source libraries for on-line audio and video input. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. openSMILE 2.0 is distributed under a research license and can be downloaded from http://opensmile.sourceforge.net/.
1,186 citations
"Automatic transfer of musical mood ..." refers methods in this paper
...The dataset also provides music features for each song, extracted by openSMILE [9]....
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25 Oct 2010
TL;DR: This work investigates and develops methods to extract and combine low-level features that represent the emotional content of an image, and uses these for image emotion classification.
Abstract: Images can affect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classification. Specifically, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are specific to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classification); and a set of peer rated abstract paintings to investigate the influence of the features and ratings on pictures without contextual content. Improved classification results are obtained on the International Affective Picture System (IAPS), compared to state of the art work.
734 citations
"Automatic transfer of musical mood ..." refers methods in this paper
...We use a dataset of abstract paintings from [13]....
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