1,063 research outputs found
On the Application of Generic Summarization Algorithms to Music
Several generic summarization algorithms were developed in the past and
successfully applied in fields such as text and speech summarization. In this
paper, we review and apply these algorithms to music. To evaluate this
summarization's performance, we adopt an extrinsic approach: we compare a Fado
Genre Classifier's performance using truncated contiguous clips against the
summaries extracted with those algorithms on 2 different datasets. We show that
Maximal Marginal Relevance (MMR), LexRank and Latent Semantic Analysis (LSA)
all improve classification performance in both datasets used for testing.Comment: 12 pages, 1 table; Submitted to IEEE Signal Processing Letter
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
Using Generic Summarization to Improve Music Information Retrieval Tasks
In order to satisfy processing time constraints, many MIR tasks process only
a segment of the whole music signal. This practice may lead to decreasing
performance, since the most important information for the tasks may not be in
those processed segments. In this paper, we leverage generic summarization
algorithms, previously applied to text and speech summarization, to summarize
items in music datasets. These algorithms build summaries, that are both
concise and diverse, by selecting appropriate segments from the input signal
which makes them good candidates to summarize music as well. We evaluate the
summarization process on binary and multiclass music genre classification
tasks, by comparing the performance obtained using summarized datasets against
the performances obtained using continuous segments (which is the traditional
method used for addressing the previously mentioned time constraints) and full
songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA,
MMR, and a Support Sets-based Centrality model improve classification
performance when compared to selected 30-second baselines. We also show that
summarized datasets lead to a classification performance whose difference is
not statistically significant from using full songs. Furthermore, we make an
argument stating the advantages of sharing summarized datasets for future MIR
research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio,
Speech and Language Processin
A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION
VIDEO segmentation facilitates e±cient video indexing and navigation in large
digital video archives. It is an important process in a content-based video
indexing and retrieval (CBVIR) system. Many automated solutions performed seg-
mentation by utilizing information about the \facts" of the video. These \facts"
come in the form of labels that describe the objects which are captured by the cam-
era. This type of solutions was able to achieve good and consistent results for some
video genres such as news programs and informational presentations. The content
format of this type of videos is generally quite standard, and automated solutions
were designed to follow these format rules. For example in [1], the presence of news
anchor persons was used as a cue to determine the start and end of a meaningful
news segment.
The same cannot be said for video genres such as movies and feature films.
This is because makers of this type of videos utilized different filming techniques to
design their videos in order to elicit certain affective response from their targeted
audience. Humans usually perform manual video segmentation by trying to relate
changes in time and locale to discontinuities in meaning [2]. As a result, viewers
usually have doubts about the boundary locations of a meaningful video segment
due to their different affective responses.
This thesis presents an entirely new view to the problem of high level video
segmentation. We developed a novel probabilistic method for affective level video
content analysis and segmentation. Our method had two stages. In the first stage,
a®ective content labels were assigned to video shots by means of a dynamic bayesian
0. Abstract 3
network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN)
topology was proposed for this stage. The topology was based on the pleasure-
arousal-dominance (P-A-D) model of a®ect representation [3]. In principle, this
model can represent a large number of emotions. In the second stage, the visual,
audio and a®ective information of the video was used to compute a statistical feature
vector to represent the content of each shot. Affective level video segmentation was
achieved by applying spectral clustering to the feature vectors.
We evaluated the first stage of our proposal by comparing its emotion detec-
tion ability with all the existing works which are related to the field of a®ective video
content analysis. To evaluate the second stage, we used the time adaptive clustering
(TAC) algorithm as our performance benchmark. The TAC algorithm was the best
high level video segmentation method [2]. However, it is a very computationally
intensive algorithm. To accelerate its computation speed, we developed a modified
TAC (modTAC) algorithm which was designed to be mapped easily onto a field
programmable gate array (FPGA) device. Both the TAC and modTAC algorithms
were used as performance benchmarks for our proposed method.
Since affective video content is a perceptual concept, the segmentation per-
formance and human agreement rates were used as our evaluation criteria. To obtain
our ground truth data and viewer agreement rates, a pilot panel study which was
based on the work of Gross et al. [4] was conducted. Experiment results will show
the feasibility of our proposed method. For the first stage of our proposal, our
experiment results will show that an average improvement of as high as 38% was
achieved over previous works. As for the second stage, an improvement of as high
as 37% was achieved over the TAC algorithm
Social network extraction and analysis based on multimodal dyadic interaction
Social interactions are a very important component in people"s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times" Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links" weights are a measure of the"influence" a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network
Linguistic challenges in automatic summarization technology
[EN] Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.Diedrichsen, E. (2017). Linguistic challenges in automatic summarization technology. Journal of Computer-Assisted Linguistic Research. 1(1):40-60. doi:10.4995/jclr.2017.7787.SWORD40601
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