6,859 research outputs found
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data
This paper presents a novel BigEAR big data framework that employs
psychological audio processing chain (PAPC) to process smartphone-based
acoustic big data collected when the user performs social conversations in
naturalistic scenarios. The overarching goal of BigEAR is to identify moods of
the wearer from various activities such as laughing, singing, crying, arguing,
and sighing. These annotations are based on ground truth relevant for
psychologists who intend to monitor/infer the social context of individuals
coping with breast cancer. We pursued a case study on couples coping with
breast cancer to know how the conversations affect emotional and social well
being. In the state-of-the-art methods, psychologists and their team have to
hear the audio recordings for making these inferences by subjective evaluations
that not only are time-consuming and costly, but also demand manual data coding
for thousands of audio files. The BigEAR framework automates the audio
analysis. We computed the accuracy of BigEAR with respect to the ground truth
obtained from a human rater. Our approach yielded overall average accuracy of
88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International
Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27,
2016, Washington DC, US
SEGAN: Speech Enhancement Generative Adversarial Network
Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn complex functions from large example sets. In this work, we propose the
use of generative adversarial networks for speech enhancement. In contrast to
current techniques, we operate at the waveform level, training the model
end-to-end, and incorporate 28 speakers and 40 different noise conditions into
the same model, such that model parameters are shared across them. We evaluate
the proposed model using an independent, unseen test set with two speakers and
20 alternative noise conditions. The enhanced samples confirm the viability of
the proposed model, and both objective and subjective evaluations confirm the
effectiveness of it. With that, we open the exploration of generative
architectures for speech enhancement, which may progressively incorporate
further speech-centric design choices to improve their performance.Comment: 5 pages, 4 figures, accepted in INTERSPEECH 201
Software Corrections of Vocal Disorders
We discuss how vocal disorders can be post-corrected via a simple nonlinear
noise reduction scheme. This work is motivated by the need of a better
understanding of voice dysfunctions. This would entail a twofold advantage for
affected patients: Physicians can perform better surgical interventions and on
the other hand researchers can try to build up devices that can help to improve
voice quality, i.e. in a phone conversation, avoiding any surgigal treatment.
As a first step, a proper signal classification is performed, through the idea
of geometric signal separation in a feature space. Then through the analysis of
the different regions populated by the samples coming from healthy people and
from patients affected by T1A glottis cancer, one is able to understand which
kind of interventions are necessary in order to correct the illness, i.e. to
move the corresponding feature vector from the sick region to the healthy one.
We discuss such a filter and show its performance.Comment: Computer Methods and Programs in Biomedicine, accepted for
publicatio
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
Exploration and Optimization of Noise Reduction Algorithms for Speech Recognition in Embedded Devices
Environmental noise present in real-life applications substantially degrades the performance of speech recognition systems. An example is an in-car scenario where a speech recognition system has to support the man-machine interface. Several sources of noise coming from the engine, wipers, wheels etc., interact with speech. Special challenge is given in an open window scenario, where noise of traffic, park noise, etc., has to be regarded. The main goal of this thesis is to improve the performance of a speech recognition system based on a state-of-the-art hidden Markov model (HMM) using noise reduction methods. The performance is measured with respect to word error rate and with the method of mutual information. The noise reduction methods are based on weighting rules. Least-squares weighting rules in the frequency domain have been developed to enable a continuous development based on the existing system and also to guarantee its low complexity and footprint for applications in embedded devices. The weighting rule parameters are optimized employing a multidimensional optimization task method of Monte Carlo followed by a compass search method. Root compression and cepstral smoothing methods have also been implemented to boost the recognition performance. The additional complexity and memory requirements of the proposed system are minimum. The performance of the proposed system was compared to the European Telecommunications Standards Institute (ETSI) standardized system. The proposed system outperforms the ETSI system by up to 8.6 % relative increase in word accuracy and achieves up to 35.1 % relative increase in word accuracy compared to the existing baseline system on the ETSI Aurora 3 German task. A relative increase of up to 18 % in word accuracy over the existing baseline system is also obtained from the proposed weighting rules on large vocabulary databases. An entropy-based feature vector analysis method has also been developed to assess the quality of feature vectors. The entropy estimation is based on the histogram approach. The method has the advantage to objectively asses the feature vector quality regardless of the acoustic modeling assumption used in the speech recognition system
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