Blind Denoising Autoencoder
نویسندگان
چکیده
منابع مشابه
Reverberant speech recognition based on denoising autoencoder
Denoising autoencoder is applied to reverberant speech recognition as a noise robust front-end to reconstruct clean speech spectrum from noisy input. In order to capture context effects of speech sounds, a window of multiple short-windowed spectral frames are concatenated to form a single input vector. Additionally, a combination of short and long-term spectra is investigated to properly handle...
متن کاملA Denoising Autoencoder that Guides Stochastic Search
An algorithm is described that adaptively learns a non-linear mutation distribution. It works by training a denoising autoencoder (DA) online at each generation of a genetic algorithm to reconstruct a slowly decaying memory of the best genotypes so far. A compressed hidden layer forces the autoencoder to learn hidden features in the training set that can be used to accelerate search on novel pr...
متن کاملSpeech enhancement based on deep denoising autoencoder
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, by using noisyclean training pairs, we further introduce a denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a...
متن کاملUsing denoising autoencoder for emotion recognition
In this paper, we propose to use the denoising autoencoder to generate robust feature representations for emotion recognition. In our method, the input of the denoising autoencoder is the normalized static feature set (state-of-the-art features for emotion recognition). This input is mapped to two hidden representations: one is to capture the neutral information from the input, and the other on...
متن کاملRelational Stacked Denoising Autoencoder for Tag Recommendation
Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2019
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2018.2838679