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Audio Based Event Detection In Videos A Survey

816 words - 4 pages

indexing and retrieval systems for identifying videos in which
few predefined events are shown.
7) Other Applications: Nahijima et.al [46] presented a quick
and precise Motion Pictures Experts Group(MPEG) audio classification
algorithm based on sub band data domain. Classification
task was carried out for 4 segments such as silent, music,
speech and applause segments for 1s unit. Later Bayesian
discrimination method for multivariate Gaussian distribution
was used for classification task.
III. SYSTEM DESCRIPTION
Digital video is a one that is generated from the camera
which is in the form of pixels. A digital video is a sequences
of images, called frames displayed at a frame rate, to create
an illusion of animation. Frame rate can be defined as number
of unique consecutive frames produced per second. Frame
rate varies between several standards. A typical video has
a frame rate of 25fps. A complete video is partitioned into
acts. Each act is further partitioned into scenes. A scene is a
sequence of actions where each consecutive frame differs with
slight change. Audio is now extracted from the given video
either at short-term frame level or at long-term clip level.
Data representation of extracted audio signal addresses the
issues of representing the examples to be classified in terms of
feature vectors. The intention of modeling is to find a mapping
from the feature space to the target labels so as to reduce the
prediction error. The general system components of the audio
based video event detection is presented in Figure 1. The major
components of the system are the audio data representation and
learning methodologies.
An audio signal can be represented by many number of
features. Audio feature extraction is an important phase for any
type of audio data. It is process of refining enormous amount
raw audio data into compact representations which holds
the higher level information of the audio. There exists large
number of audio features that are suitable for various such
as audio retrieval, audio segmentation, music and information
retrieval, environmental sound retrieval etc. Eyben et.al [14]
presented various feature extractor tools which can be used for
many applications. Some of the tools to extract audio features
are shown in Table I.
Tools References
jAudio [25]
Opensmile [47]
PRAAT [49]
YAAFE [67]
TABLE I. FEW AUDIO FEATURE EXTRACTION TOOL-KIT
Modeling data can be organized based on the desired output
of the algorithm or on the type of input examples. Supervised
learning is a method of learning where...

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