23,295 research outputs found
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Advanced video classification systems decode video frames to derive the
necessary texture and motion representations for ingestion and analysis by
spatio-temporal deep convolutional neural networks (CNNs). However, when
considering visual Internet-of-Things applications, surveillance systems and
semantic crawlers of large video repositories, the video capture and the
CNN-based semantic analysis parts do not tend to be co-located. This
necessitates the transport of compressed video over networks and incurs
significant overhead in bandwidth and energy consumption, thereby significantly
undermining the deployment potential of such systems. In this paper, we
investigate the trade-off between the encoding bitrate and the achievable
accuracy of CNN-based video classification models that directly ingest
AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video
bitstreams and applying complex optical flow calculations prior to CNN
processing, we only retain motion vector and select texture information at
significantly-reduced bitrates and apply no additional processing prior to CNN
ingestion. Based on three CNN architectures and two action recognition
datasets, we achieve 11%-94% saving in bitrate with marginal effect on
classification accuracy. A model-based selection between multiple CNNs
increases these savings further, to the point where, if up to 7% loss of
accuracy can be tolerated, video classification can take place with as little
as 3 kbps for the transport of the required compressed video information to the
system implementing the CNN models
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor
We investigate video classification via a two-stream convolutional neural
network (CNN) design that directly ingests information extracted from
compressed video bitstreams. Our approach begins with the observation that all
modern video codecs divide the input frames into macroblocks (MBs). We
demonstrate that selective access to MB motion vector (MV) information within
compressed video bitstreams can also provide for selective, motion-adaptive, MB
pixel decoding (a.k.a., MB texture decoding). This in turn allows for the
derivation of spatio-temporal video activity regions at extremely high speed in
comparison to conventional full-frame decoding followed by optical flow
estimation. In order to evaluate the accuracy of a video classification
framework based on such activity data, we independently train two CNN
architectures on MB texture and MV correspondences and then fuse their scores
to derive the final classification of each test video. Evaluation on two
standard datasets shows that the proposed approach is competitive to the best
two-stream video classification approaches found in the literature. At the same
time: (i) a CPU-based realization of our MV extraction is over 977 times faster
than GPU-based optical flow methods; (ii) selective decoding is up to 12 times
faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs
perform inference at 5 to 49 times lower cloud computing cost than the fastest
methods from the literature.Comment: Accepted in IEEE Transactions on Circuits and Systems for Video
Technology. Extension of ICIP 2017 conference pape
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