2,597 research outputs found
Towards Affordable Disclosure of Spoken Word Archives
This paper presents and discusses ongoing work aiming at affordable disclosure of real-world spoken word archives in general, and in particular of a collection of recorded interviews with Dutch survivors of World War II concentration camp Buchenwald. Given such collections, the least we want to be able to provide is search at different levels and a flexible way of presenting results. Strategies for automatic annotation based on speech recognition – supporting e.g., within-document search– are outlined and discussed with respect to the Buchenwald interview collection. In addition, usability aspects of the spoken word search are discussed on the basis of our experiences with the online Buchenwald web portal. It is concluded that, although user feedback is generally fairly positive, automatic annotation performance is still far from satisfactory, and requires additional research
Psychophysiology in games
Psychophysiology is the study of the relationship between psychology
and its physiological manifestations. That relationship is of particular importance
for both game design and ultimately gameplaying. Players’ psychophysiology offers
a gateway towards a better understanding of playing behavior and experience.
That knowledge can, in turn, be beneficial for the player as it allows designers to
make better games for them; either explicitly by altering the game during play or
implicitly during the game design process. This chapter argues for the importance
of physiology for the investigation of player affect in games, reviews the current
state of the art in sensor technology and outlines the key phases for the application
of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject
(project no: 318803).peer-reviewe
Audio Event Detection using Weakly Labeled Data
Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.Comment: ACM Multimedia 201
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding
Voice communication between air traffic controllers (ATCos) and pilots is
critical for ensuring safe and efficient air traffic control (ATC). This task
requires high levels of awareness from ATCos and can be tedious and
error-prone. Recent attempts have been made to integrate artificial
intelligence (AI) into ATC in order to reduce the workload of ATCos. However,
the development of data-driven AI systems for ATC demands large-scale annotated
datasets, which are currently lacking in the field. This paper explores the
lessons learned from the ATCO2 project, a project that aimed to develop a
unique platform to collect and preprocess large amounts of ATC data from
airspace in real time. Audio and surveillance data were collected from publicly
accessible radio frequency channels with VHF receivers owned by a community of
volunteers and later uploaded to Opensky Network servers, which can be
considered an "unlimited source" of data. In addition, this paper reviews
previous work from ATCO2 partners, including (i) robust automatic speech
recognition, (ii) natural language processing, (iii) English language
identification of ATC communications, and (iv) the integration of surveillance
data such as ADS-B. We believe that the pipeline developed during the ATCO2
project, along with the open-sourcing of its data, will encourage research in
the ATC field. A sample of the ATCO2 corpus is available on the following
website: https://www.atco2.org/data, while the full corpus can be purchased
through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We
demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when
little or near to no ATC in-domain data is available. For instance, with the
CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9%
WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but
supervised CNN-TDNNf model.Comment: Manuscript under revie
Deep Active Learning Explored Across Diverse Label Spaces
abstract: Deep learning architectures have been widely explored in computer vision and have
depicted commendable performance in a variety of applications. A fundamental challenge
in training deep networks is the requirement of large amounts of labeled training
data. While gathering large quantities of unlabeled data is cheap and easy, annotating
the data is an expensive process in terms of time, labor and human expertise.
Thus, developing algorithms that minimize the human effort in training deep models
is of immense practical importance. Active learning algorithms automatically identify
salient and exemplar samples from large amounts of unlabeled data and can augment
maximal information to supervised learning models, thereby reducing the human annotation
effort in training machine learning models. The goal of this dissertation is to
fuse ideas from deep learning and active learning and design novel deep active learning
algorithms. The proposed learning methodologies explore diverse label spaces to
solve different computer vision applications. Three major contributions have emerged
from this work; (i) a deep active framework for multi-class image classication, (ii)
a deep active model with and without label correlation for multi-label image classi-
cation and (iii) a deep active paradigm for regression. Extensive empirical studies
on a variety of multi-class, multi-label and regression vision datasets corroborate the
potential of the proposed methods for real-world applications. Additional contributions
include: (i) a multimodal emotion database consisting of recordings of facial
expressions, body gestures, vocal expressions and physiological signals of actors enacting
various emotions, (ii) four multimodal deep belief network models and (iii)
an in-depth analysis of the effect of transfer of multimodal emotion features between
source and target networks on classification accuracy and training time. These related
contributions help comprehend the challenges involved in training deep learning
models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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