993 research outputs found
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Deep speech inpainting of time-frequency masks
Transient loud intrusions, often occurring in noisy environments, can
completely overpower speech signal and lead to an inevitable loss of
information. While existing algorithms for noise suppression can yield
impressive results, their efficacy remains limited for very low signal-to-noise
ratios or when parts of the signal are missing. To address these limitations,
here we propose an end-to-end framework for speech inpainting, the
context-based retrieval of missing or severely distorted parts of
time-frequency representation of speech. The framework is based on a
convolutional U-Net trained via deep feature losses, obtained using speechVGG,
a deep speech feature extractor pre-trained on an auxiliary word classification
task. Our evaluation results demonstrate that the proposed framework can
recover large portions of missing or distorted time-frequency representation of
speech, up to 400 ms and 3.2 kHz in bandwidth. In particular, our approach
provided a substantial increase in STOI & PESQ objective metrics of the
initially corrupted speech samples. Notably, using deep feature losses to train
the framework led to the best results, as compared to conventional approaches.Comment: Accepted to InterSpeech202
Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data
This paper introduces a method based on a deep neural network (DNN) that is
perfectly capable of processing radar data from extremely thinned radar
apertures. The proposed DNN processing can provide both aliasing-free radar
imaging and super-resolution. The results are validated by measuring the
detection performance on realistic simulation data and by evaluating the
Point-Spread-function (PSF) and the target-separation performance on measured
point-like targets. Also, a qualitative evaluation of a typical automotive
scene is conducted. It is shown that this approach can outperform
state-of-the-art subspace algorithms and also other existing machine learning
solutions. The presented results suggest that machine learning approaches
trained with sufficiently sophisticated virtual input data are a very promising
alternative to compressed sensing and subspace approaches in radar signal
processing. The key to this performance is that the DNN is trained using
realistic simulation data that perfectly mimic a given sparse antenna radar
array hardware as the input. As ground truth, ultra-high resolution data from
an enhanced virtual radar are simulated. Contrary to other work, the DNN
utilizes the complete radar cube and not only the antenna channel information
at certain range-Doppler detections. After training, the proposed DNN is
capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers
nearly the same resolution and image quality as would be achieved with a fully
occupied array.Comment: 15 pages, 12 figures, Accepted to IEEE Journal of Microwave
Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function
Recent research advances in deep neural network (DNN)-based beamformers have
shown great promise for speech enhancement under adverse acoustic conditions.
Different network architectures and input features have been explored in
estimating beamforming weights. In this paper, we propose a deep beamformer
based on an efficient convolutional recurrent network (CRN) trained with a
novel ARray RespOnse-aWare (ARROW) loss function. The ARROW loss exploits the
array responses of the target and interferer by using the ground truth relative
transfer functions (RTFs). The DNN-based beamforming system, trained with ARROW
loss through supervised learning, is able to perform speech enhancement and
speaker localization jointly. Experimental results have shown that the proposed
deep beamformer, trained with the linearly weighted scale-invariant
source-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superior
performance in speech enhancement and speaker localization compared to two
baselines.Comment: 6 page
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
An Efficient Optimal Reconstruction Based Speech Separation Based on Hybrid Deep Learning Technique
Conventional single-channel speech separation has two long-standing issues. The first issue, over-smoothing,
is addressed, and estimated signals are used to expand the training data set. Second, DNN generates prior knowledge to address the problem of incomplete separation and mitigate speech distortion. To overcome all current issues, we suggest employing an efficient optimal reconstruction-based speech separation (ERSS) to overcome those problems using a hybrid deep learning technique. First, we propose an integral fox ride optimization (IFRO) algorithm for spectral structure reconstruction with the help of multiple spectrum features: time dynamic information, binaural and mono features. Second, we introduce a hybrid retrieval-based deep neural network (RDNN) to reconstruct the spectrograms size of speech and noise directly. The input signals are sent to Short Term Fourier Transform (STFT).
STFT converts a clean input signal into spectrograms then uses a feature extraction technique called IFRO to extract features from spectrograms. After extracting the features, using the RDNN classification algorithm, the classified features are converted to softmax. ISTFT then applies to softmax and correctly separates speech signals. Experiments show that our proposed method achieves the highest gains in SDR, SIR, SAR STIO, and PESQ outcomes of 10.9, 15.3, 10.8, 0.08, and 0.58, respectively. The Joint-DNN-SNMF obtains 9.6, 13.4, 10.4, 0.07, and 0.50, comparable to the Joint-DNN-SNMF. The proposed result is compared to a different method and some previous work. In comparison to previous research, our proposed methodology yields better results
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