755 research outputs found
Autoencoding sensory substitution
Tens of millions of people live blind, and their number is ever increasing. Visual-to-auditory sensory substitution (SS) encompasses a family of cheap, generic solutions to assist the visually impaired by conveying visual information through sound. The required SS training is lengthy: months of effort is necessary to reach a practical level of adaptation. There are two reasons for the tedious training process: the elongated substituting audio signal, and the disregard for the compressive characteristics of the human hearing system.
To overcome these obstacles, we developed a novel class of SS methods, by training deep recurrent autoencoders for image-to-sound conversion. We successfully trained deep learning models on different datasets to execute visual-to-auditory stimulus conversion. By constraining the visual space, we demonstrated the viability of shortened substituting audio signals, while proposing mechanisms, such as the integration of computational hearing models, to optimally convey visual features in the substituting stimulus as perceptually discernible auditory components. We tested our approach in two separate cases. In the first experiment, the author went blindfolded for 5 days, while performing SS training on hand posture discrimination. The second experiment assessed the accuracy of reaching movements towards objects on a table. In both test cases, above-chance-level accuracy was attained after a few hours of training.
Our novel SS architecture broadens the horizon of rehabilitation methods engineered for the visually impaired. Further improvements on the proposed model shall yield hastened rehabilitation of the blind and a wider adaptation of SS devices as a consequence
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
Exploring Audio Sensing in Detecting Social Interactions Using Smartphone Devices
In recent years, the fast proliferation of smartphones devices has provided powerful and portable methodologies for integrating sensing systems which can run continuously and provide feedback in real-time. The mobile crowd-sensing of human behaviour is an emerging computing paradigm that offers a challenge of sensing everyday social interactions performed by people who carry smartphone devices upon themselves. Typical smartphone sensors and the mobile crowd-sensing paradigm compose a process where the sensors present, such as the microphone, are used to infer social relationships between people in diverse social settings, where environmental factors can be dynamic and the infrastructure of buildings can vary.
The typical approaches in detecting social interactions between people consider the use of co-location as a proxy for real-world interactions. Such approaches can under-perform in challenging situations where multiple social interactions can occur within close proximity to each other, for example when people are in a queue at the supermarket but not a part of the same social interaction. Other approaches involve a limitation where all participants of a social interaction must carry a smartphone device with themselves at all times and each smartphone must have the sensing app installed. The problem here is the feasibility of the sensing system, which relies heavily on each participant's smartphone acting as nodes within a social graph, connected together with weighted edges of proximity between the devices; when users uninstall the app or disable background sensing, the system is unable to accurately determine the correct number of participants.
In this thesis, we present two novel approaches to detecting co-located social interac- tions using smartphones. The first relies on the use of WiFi signals and audio signals
to distinguish social groups interacting within a few meters from each other with 88% precision. We orchestrated preliminary experiments using WiFi as a proxy for co-location between people who are socially interacting. Initial results showed that in more challenging scenarios, WiFi is not accurate enough to determine if people are socially interacting within the same social group. We then made use of audio as a second modality to capture the sound patterns of conversations to identify and segment social groups within close proximity to each other. Through a range of real-world experiments (social interactions in meeting scenarios, coffee shop scenarios, conference scenarios), we demonstrate a technique that utilises WiFi fingerprinting, along with sound fingerprinting to identify these social groups. We built a system which performs well, and then optimized the power consumption and improved the performance to 88% precision in the most challenging scenarios using duty cycling and data averaging techniques.
The second approach explores the feasibility of detecting social interactions without the need of all social contacts to carry a social sensing device. This work explores the use of supervised and unsupervised Deep Learning techniques before concluding on the use of an Autoencoder model to perform a Speaker Identification task. We demonstrate how machine learning can be used with the audio data collected from a singular device as a speaker identification framework. Speech from people is used as the input to our Autoencoder model and then classified against a list of "social contacts" to determine if the user has spoken a person before or not. By doing this, the system can count the number of social contacts belonging to the user, and develop a database of common social contacts. Through the use 100 randomly-generated social conversations and the use of state-of-the-art Deep Learning techniques, we demonstrate how this system can accurately distinguish new and existing speakers from a data set of voices, to count the number of daily social interactions a user encounters with a precision of 75%. We then optimize the model using Hyperparameter Optimization to ensure that the model is most optimal for the task. Unlike most systems in the literature, this approach would work without the need to modify the existing infrastructure of a building, and without all participants needing to install the same ap
Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition
This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network
Bio-motivated features and deep learning for robust speech recognition
Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech
Recognition (ASR) technologies has experienced over the last five years
their performance under hard environmental condition is still far from
that of humans preventing their adoption in several real applications.
In this thesis the challenge of robustness of modern automatic speech
recognition systems is addressed following two main research lines.
The first one focuses on modeling the human auditory system to
improve the robustness of the feature extraction stage yielding to novel
auditory motivated features. Two main contributions are produced.
On the one hand, a model of the masking behaviour of the Human
Auditory System (HAS) is introduced, based on the non-linear filtering
of a speech spectro-temporal representation applied simultaneously
to both frequency and time domains. This filtering is accomplished
by using image processing techniques, in particular mathematical
morphology operations with an specifically designed Structuring Element
(SE) that closely resembles the masking phenomena that take
place in the cochlea. On the other hand, the temporal patterns of
auditory-nerve firings are modeled. Most conventional acoustic features
are based on short-time energy per frequency band discarding
the information contained in the temporal patterns. Our contribution
is the design of several types of feature extraction schemes based on
the synchrony effect of auditory-nerve activity, showing that the modeling
of this effect can indeed improve speech recognition accuracy in
the presence of additive noise. Both models are further integrated into
the well known Power Normalized Cepstral Coefficients (PNCC).
The second research line addresses the problem of robustness in
noisy environments by means of the use of Deep Neural Networks
(DNNs)-based acoustic modeling and, in particular, of Convolutional
Neural Networks (CNNs) architectures. A deep residual network
scheme is proposed and adapted for our purposes, allowing Residual
Networks (ResNets), originally intended for image processing tasks,
to be used in speech recognition where the network input is small
in comparison with usual image dimensions. We have observed that
ResNets on their own already enhance the robustness of the whole system
against noisy conditions. Moreover, our experiments demonstrate
that their combination with the auditory motivated features devised
in this thesis provide significant improvements in recognition accuracy
in comparison to other state-of-the-art CNN-based ASR systems
under mismatched conditions, while maintaining the performance in
matched scenarios.
The proposed methods have been thoroughly tested and compared
with other state-of-the-art proposals for a variety of datasets and
conditions. The obtained results prove that our methods outperform
other state-of-the-art approaches and reveal that they are suitable for
practical applications, specially where the operating conditions are
unknown.El objetivo de esta tesis se centra en proponer soluciones al problema
del reconocimiento de habla robusto; por ello, se han llevado a cabo
dos líneas de investigación.
En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento
del sistema auditivo humano, modelando especialmente los fenómenos
de enmascaramiento y sincronía. En la segunda, se propone mejorar
las tasas de reconocimiento mediante el uso de técnicas de
aprendizaje profundo, en conjunto con las características propuestas.
Los métodos propuestos tienen como principal objetivo, mejorar la
precisión del sistema de reconocimiento cuando las condiciones de
operación no son conocidas, aunque el caso contrario también ha sido
abordado.
En concreto, nuestras principales propuestas son los siguientes:
Simular el sistema auditivo humano con el objetivo de mejorar
la tasa de reconocimiento en condiciones difíciles, principalmente
en situaciones de alto ruido, proponiendo esquemas de
extracción de características novedosos.
Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación:
• Modelar el comportamiento de enmascaramiento del sistema
auditivo humano, usando técnicas del procesado de
imagen sobre el espectro, en concreto, llevando a cabo el
diseño de un filtro morfológico que captura este efecto.
• Modelar el efecto de la sincroní que tiene lugar en el nervio
auditivo.
• La integración de ambos modelos en los conocidos Power
Normalized Cepstral Coefficients (PNCC).
La aplicación de técnicas de aprendizaje profundo con el objetivo
de hacer el sistema más robusto frente al ruido, en particular
con el uso de redes neuronales convolucionales profundas, como
pueden ser las redes residuales.
Por último, la aplicación de las características propuestas en
combinación con las redes neuronales profundas, con el objetivo
principal de obtener mejoras significativas, cuando las condiciones
de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ
Deep Learning for Distant Speech Recognition
Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past decades, however, a natural and robust human-machine speech
interaction still appears to be out of reach, especially when users interact
with a distant microphone in noisy and reverberant environments. The latter
disturbances severely hamper the intelligibility of a speech signal, making
Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques,
architectures, and algorithms to improve the robustness of distant-talking
acoustic models. We first elaborate on methodologies for realistic data
contamination, with a particular emphasis on DNN training with simulated data.
We then investigate on approaches for better exploiting speech contexts,
proposing some original methodologies for both feed-forward and recurrent
neural networks. Lastly, inspired by the idea that cooperation across different
DNNs could be the key for counteracting the harmful effects of noise and
reverberation, we propose a novel deep learning paradigm called network of deep
neural networks. The analysis of the original concepts were based on extensive
experimental validations conducted on both real and simulated data, considering
different corpora, microphone configurations, environments, noisy conditions,
and ASR tasks.Comment: PhD Thesis Unitn, 201
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