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Music Information Retrieval by Detecting Mood via Computational Media Aesthetics
- Computational Media Aesthetics”, IEEE/WIC International Conference on Web Intelligence
, 2003
"... It is well known that music can convey emotion and modulate mood, to retrieval music by mood is sometimes the exclusive manner people select music to enjoy. This paper concentrates on music retrieval by detecting mood. Mood detection is implemented on the viewpoint of Computational Media Aesthetics, ..."
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It is well known that music can convey emotion and modulate mood, to retrieval music by mood is sometimes the exclusive manner people select music to enjoy. This paper concentrates on music retrieval by detecting mood. Mood detection is implemented on the viewpoint of Computational Media Aesthetics, that is, by analyzing two music dimensions, tempo and articulation, in the procedure of making music, we derive four categories of mood, happiness, anger, sadness and fear. Concretely, with regard to music in the format of raw audio, after tempo is detected using a multiple-agent approach, a feature called relative tempo is calculated, and after the mean and standard deviation of the feature called average silence ratio in the presented computational articulation model are calculated, a simple BP neural network classifier is trained to detect mood. Users retrieval music by browsing the 3D visualization of feature space associated with specific mood. This paper reports the experimental result on a test corpus of 353 pieces of popular music with various genres. 1.
Color-Mood Analysis of Films Based on Syntactic and Psychological Models
"... The emergence of peer-to-peer networking and the increase of home PC storage capacity are necessitating efficient scaleable methods for video clustering, recommending and browsing. Based on film theories and psychological models, color-mood is an important factor affecting user emotional preferences ..."
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The emergence of peer-to-peer networking and the increase of home PC storage capacity are necessitating efficient scaleable methods for video clustering, recommending and browsing. Based on film theories and psychological models, color-mood is an important factor affecting user emotional preferences. We propose a compact set of features for color-mood analysis and subgenre discrimination. We introduce two color representations for scenes and full films in order to extract the essential moods from the films: a global measure for the color palette and a discriminative measure for the transitions of the moods in the movie. We captured the dominant color ratio and the pace of the movie. Despite the simplicity and efficiency of the features, the classification accuracy was surprisingly good, about 80%, possibly thanks to the prevalence of the color-mood association in feature films. 1.
Discovering semantics from the visualization of film takes
- in Accepted for IEEE Multimedia Modelling 2004
, 2004
"... In this paper, we study the application of a scene structure visualizing technique called Double-Ring Take-Transition-Diagram (DR-TTD). This technique presents takes and their transitions during a film scene via nodes and edges of a ‘graph ’ consisting of two rings as its back-bone. We describe how ..."
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In this paper, we study the application of a scene structure visualizing technique called Double-Ring Take-Transition-Diagram (DR-TTD). This technique presents takes and their transitions during a film scene via nodes and edges of a ‘graph ’ consisting of two rings as its back-bone. We describe how certain filmic elements such as mon-tage, centre/cutaway, dialogue, temporal flow, zone change, dramatic progression, shot association, scene introduction, scene resolution, master shot and editing orchestration can be identified from a scene through the signature arrange-ments of nodes and edges in the DR-TTD. 1
c © 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. Extraction of Film Takes for Cinematic Analysis
"... Abstract. In this paper, we focus on the ‘reverse editing ’ problem in movie analysis, i.e., the extraction of film takes, original camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonline ..."
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Abstract. In this paper, we focus on the ‘reverse editing ’ problem in movie analysis, i.e., the extraction of film takes, original camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonlinear browsing, content annotation and the extraction of higher order cinematic constructs from film. A two-part framework for take extraction is proposed. The first part focuses on the filtering out action-driven scenes for which take extraction is not useful. The second part focuses on extracting film takes using agglomerative hierarchical clustering methods along with different similarity metrics and group distances and demonstrates our findings with 10 movies.
IDENTIFYING FILM TAKES FOR CINEMATIC ANALYSIS
"... In this paper, we focus on the ‘reverse editing ’ problem in movie analysis, i.e., the extraction of film takes, origi-nal camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonlinear brows- ..."
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In this paper, we focus on the ‘reverse editing ’ problem in movie analysis, i.e., the extraction of film takes, origi-nal camera shots that a film editor extracts and arranges to produce a finished scene. The ability to disassemble final scenes and shots into takes is essential for nonlinear brows-ing, content annotation and the extraction of higher order cinematic constructs from film. In this work, we inves-tigate agglomerative hierachical clustering methods along with different similarity metrics and group distances for this task, and demonstrate our findings with 10 movies. 1.
