Jul 5, 2018 · This paper aims to classify noisy sound samples in several daily indoor and outdoor acoustic scenes using an optimized deep neural networks ...
This paper aims to classify noisy sound samples in several daily indoor and outdoor acoustic scenes using an optimized deep neural networks (DNNs).
In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of ...
This section con- centrates on how SVMs can be applied to tasks where there is sequence data, for example speech recognition. One of the issues with applying ...
Missing: Deep | Show results with:Deep
Robust Noisy Speech Recognition Using Deep Neural Support Vector Machines ... Structured Support Vector Machines for Noise Robust Continuous Speech Recognition.
A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data.
The new DNN instead uses a support vector ma- chine (SVM) at the top layer. Two training algorithms are proposed at the frame and sequence-level to learn ...
Jan 7, 2015 · This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features.
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Oct 22, 2024 · We propose a new noise robust speech recognition system using time-frequency domain analysis and radial basis function (RBF) support vector ...
Missing: Deep | Show results with:Deep
Abstract. Deep Neural Network (DNN) based acoustic models have shown significant improvement over their Gaussian Mixture. Model (GMM) counterparts in the ...
Missing: Support | Show results with:Support