2,970 research outputs found
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
In this paper we propose the utterance-level Permutation Invariant Training
(uPIT) technique. uPIT is a practically applicable, end-to-end, deep learning
based solution for speaker independent multi-talker speech separation.
Specifically, uPIT extends the recently proposed Permutation Invariant Training
(PIT) technique with an utterance-level cost function, hence eliminating the
need for solving an additional permutation problem during inference, which is
otherwise required by frame-level PIT. We achieve this using Recurrent Neural
Networks (RNNs) that, during training, minimize the utterance-level separation
error, hence forcing separated frames belonging to the same speaker to be
aligned to the same output stream. In practice, this allows RNNs, trained with
uPIT, to separate multi-talker mixed speech without any prior knowledge of
signal duration, number of speakers, speaker identity or gender. We evaluated
uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks
and found that uPIT outperforms techniques based on Non-negative Matrix
Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and
compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network
(DANet). Furthermore, we found that models trained with uPIT generalize well to
unseen speakers and languages. Finally, we found that a single model, trained
with uPIT, can handle both two-speaker, and three-speaker speech mixtures
Whole Word Phonetic Displays for Speech Articulation Training
The main objective of this dissertation is to investigate and develop speech recognition technologies for speech training for people with hearing impairments. During the course of this work, a computer aided speech training system for articulation speech training was also designed and implemented. The speech training system places emphasis on displays to improve children\u27s pronunciation of isolated Consonant-Vowel-Consonant (CVC) words, with displays at both the phonetic level and whole word level. This dissertation presents two hybrid methods for combining Hidden Markov Models (HMMs) and Neural Networks (NNs) for speech recognition. The first method uses NN outputs as posterior probability estimators for HMMs. The second method uses NNs to transform the original speech features to normalized features with reduced correlation. Based on experimental testing, both of the hybrid methods give higher accuracy than standard HMM methods. The second method, using the NN to create normalized features, outperforms the first method in terms of accuracy. Several graphical displays were developed to provide real time visual feedback to users, to help them to improve and correct their pronunciations
Review of Research on Speech Technology: Main Contributions From Spanish Research Groups
In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en TecnologĂas del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose
is to extract either one or more target speech signals, respectively, from a
mixture of sounds generated by several sources. Traditionally, these tasks have
been tackled using signal processing and machine learning techniques applied to
the available acoustic signals. Since the visual aspect of speech is
essentially unaffected by the acoustic environment, visual information from the
target speakers, such as lip movements and facial expressions, has also been
used for speech enhancement and speech separation systems. In order to
efficiently fuse acoustic and visual information, researchers have exploited
the flexibility of data-driven approaches, specifically deep learning,
achieving strong performance. The ceaseless proposal of a large number of
techniques to extract features and fuse multimodal information has highlighted
the need for an overview that comprehensively describes and discusses
audio-visual speech enhancement and separation based on deep learning. In this
paper, we provide a systematic survey of this research topic, focusing on the
main elements that characterise the systems in the literature: acoustic
features; visual features; deep learning methods; fusion techniques; training
targets and objective functions. In addition, we review deep-learning-based
methods for speech reconstruction from silent videos and audio-visual sound
source separation for non-speech signals, since these methods can be more or
less directly applied to audio-visual speech enhancement and separation.
Finally, we survey commonly employed audio-visual speech datasets, given their
central role in the development of data-driven approaches, and evaluation
methods, because they are generally used to compare different systems and
determine their performance
Segregating Event Streams and Noise with a Markov Renewal Process Model
DS and MP are supported by EPSRC Leadership Fellowship EP/G007144/1
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