3,063 research outputs found
Phonetic Searching
An improved method and apparatus is disclosed which uses probabilistic techniques to map an input search string with a prestored audio file, and recognize certain portions of a search string phonetically. An improved interface is disclosed which permits users to input search strings, linguistics, phonetics, or a combination of both, and also allows logic functions to be specified by indicating how far separated specific phonemes are in time.Georgia Tech Research Corporatio
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
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
Decoupling Recognition from Detection: Single Shot Self-Reliant Scene Text Spotter
Typical text spotters follow the two-stage spotting strategy: detect the
precise boundary for a text instance first and then perform text recognition
within the located text region. While such strategy has achieved substantial
progress, there are two underlying limitations. 1) The performance of text
recognition depends heavily on the precision of text detection, resulting in
the potential error propagation from detection to recognition. 2) The RoI
cropping which bridges the detection and recognition brings noise from
background and leads to information loss when pooling or interpolating from
feature maps. In this work we propose the single shot Self-Reliant Scene Text
Spotter (SRSTS), which circumvents these limitations by decoupling recognition
from detection. Specifically, we conduct text detection and recognition in
parallel and bridge them by the shared positive anchor point. Consequently, our
method is able to recognize the text instances correctly even though the
precise text boundaries are challenging to detect. Additionally, our method
reduces the annotation cost for text detection substantially. Extensive
experiments on regular-shaped benchmark and arbitrary-shaped benchmark
demonstrate that our SRSTS compares favorably to previous state-of-the-art
spotters in terms of both accuracy and efficiency.Comment: To be appeared in the Proceedings of the ACM International Conference
on Multimedia (ACM MM), 202
GTH-UPM system for search on speech evaluation
This paper describes the GTH-UPM system for the Albayzin 2014 Search on Speech Evaluation. Teh evaluation task consists of searching a list of terms/queries in audio files. The GTH-UPM system we are presenting is based on a LVCSR (Large Vocabulary Continuous Speech Recognition) system. We have used MAVIR corpus and the Spanish partition of the EPPS (European Parliament Plenary Sessions) database for training both acoustic and language models. The main effort has been focused on lexicon preparation and text selection for the language model construction. The system makes use of different lexicon and language models depending on the task that is performed. For the best configuration of the system on the development set, we have obtained a FOM of 75.27 for the deyword spotting task
Enhanced Characterness for Text Detection in the Wild
Text spotting is an interesting research problem as text may appear at any
random place and may occur in various forms. Moreover, ability to detect text
opens the horizons for improving many advanced computer vision problems. In
this paper, we propose a novel language agnostic text detection method
utilizing edge enhanced Maximally Stable Extremal Regions in natural scenes by
defining strong characterness measures. We show that a simple combination of
characterness cues help in rejecting the non text regions. These regions are
further fine-tuned for rejecting the non-textual neighbor regions.
Comprehensive evaluation of the proposed scheme shows that it provides
comparative to better generalization performance to the traditional methods for
this task
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