Horror movie trailer sounds8/1/2023 In this article I present a data set with data on multiple elements and comprising audio, colour, movement, and editing data for trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015, describing the main features of the data set and how it was created. Furthermore, those features only represent some computable aspects of film style, with other key aspects not included: the aesthetic dimensions of the mmtf-14k data set, for example, relate to sound, colour, texture, and object recognition and does not include any cinematic (i.e., motion or editing) data. Second, data sets containing aesthetic information about trailers such as the mmtf-14k data set ( Deldjoo et al., 2018) and the lmtd ( Wehrmann & Barros, 2017) are designed for classifying trailers for movie recommendation systems, and so the aesthetic data they make available has been reduced to feature vectors suitable for processing by machine learning algorithms. First, some data sets contain no data about or derived from movie trailers: although both the MovieLens ( Abu-El-Haija et al., 2018) and MovieNet ( Huang et al., 2020) data sets include useful data on other aspects of the cinema, the only information relating to trailers available are the YouTube id s of the films listed in the main part of the data sets. None of the extant movie trailer data sets available were designed to answer questions about film style and are not suitable for computational film analysis for two reasons. Therefore, making data derived from films under analysis available along with a description of the methods by which that data was collected and processed is a key part of computational film analysis, promoting collaboration, innovation in approaches to analysing film style, reproducibility, and transparency. Distributing films as a corpus is not possible due to copyright restrictions. Computational film analysis lies squarely within the digital humanities, and aims to answer questions about film style framed from within the tradition of the humanities using computational methods ( Heftberger, 2018 Redfern 2020a), employing the methods and tools of statistics, data science, information visualisation, and computer science in order to understand the formal properties of the cinema. Our purpose in analysing the style of a film is “to explain why an individual motion picture is the way it is: why it has the elements of style it does and why they stand in the relations that they do” ( Carroll, 2009, p. Film style can be divided into four broad categories: mise-en-scène, cinematography, editing, and audio (see Figure 1). David Bordwell describes film style as “a film’s systematic and significant use of the techniques of the medium … Style is, minimally, the texture of the film’s images and sounds, the result of choices made by filmmaker(s) in particular historical circumstances” ( 1997, p.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |