5,475 research outputs found
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh
International Conferences on Pervasive Patterns and Applications (PATTERNS
2015), Mar 2015, Nice, Franc
Vectors of Locally Aggregated Centers for Compact Video Representation
We propose a novel vector aggregation technique for compact video
representation, with application in accurate similarity detection within large
video datasets. The current state-of-the-art in visual search is formed by the
vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates
compact video representations based on scale-invariant feature transform (SIFT)
vectors (extracted per frame) and local feature centers computed over a
training set. With the aim to increase robustness to visual distortions, we
propose a new approach that operates at a coarser level in the feature
representation. We create vectors of locally aggregated centers (VLAC) by first
clustering SIFT features to obtain local feature centers (LFCs) and then
encoding the latter with respect to given centers of local feature centers
(CLFCs), extracted from a training set. The sum-of-differences between the LFCs
and the CLFCs are aggregated to generate an extremely-compact video description
used for accurate video segment similarity detection. Experimentation using a
video dataset, comprising more than 1000 minutes of content from the Open Video
Project, shows that VLAC obtains substantial gains in terms of mean Average
Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al.,
under the same compaction factor and the same set of distortions.Comment: Proc. IEEE International Conference on Multimedia and Expo, ICME
2015, Torino, Ital
DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
This paper proposes DeepMarks, a novel end-to-end framework for systematic
fingerprinting in the context of Deep Learning (DL). Remarkable progress has
been made in the area of deep learning. Sharing the trained DL models has
become a trend that is ubiquitous in various fields ranging from biomedical
diagnosis to stock prediction. As the availability and popularity of
pre-trained models are increasing, it is critical to protect the Intellectual
Property (IP) of the model owner. DeepMarks introduces the first fingerprinting
methodology that enables the model owner to embed unique fingerprints within
the parameters (weights) of her model and later identify undesired usages of
her distributed models. The proposed framework embeds the fingerprints in the
Probability Density Function (pdf) of trainable weights by leveraging the extra
capacity available in contemporary DL models. DeepMarks is robust against
fingerprints collusion as well as network transformation attacks, including
model compression and model fine-tuning. Extensive proof-of-concept evaluations
on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural
networks architectures such as Wide Residual Networks (WRNs) and Convolutional
Neural Networks (CNNs), corroborate the effectiveness and robustness of
DeepMarks framework
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
A quick search method for audio signals based on a piecewise linear representation of feature trajectories
This paper presents a new method for a quick similarity-based search through
long unlabeled audio streams to detect and locate audio clips provided by
users. The method involves feature-dimension reduction based on a piecewise
linear representation of a sequential feature trajectory extracted from a long
audio stream. Two techniques enable us to obtain a piecewise linear
representation: the dynamic segmentation of feature trajectories and the
segment-based Karhunen-L\'{o}eve (KL) transform. The proposed search method
guarantees the same search results as the search method without the proposed
feature-dimension reduction method in principle. Experiment results indicate
significant improvements in search speed. For example the proposed method
reduced the total search time to approximately 1/12 that of previous methods
and detected queries in approximately 0.3 seconds from a 200-hour audio
database.Comment: 20 pages, to appear in IEEE Transactions on Audio, Speech and
Language Processin
- …