Audio-Noise Power Spectral Density Estimation Using Long Short-Term Memory
نویسندگان
چکیده
منابع مشابه
Noise Power Spectral Density Estimation on Highly Correlated Data
In this contribution the Minimum Statistics noise power spectral density estimator [1] is revised for the particular case of highly correlated data which is observed for example when framewise processing with considerable frame overlap is performed. For this special case the noise power estimator tends to underestimate the noise power. We identify the variance estimator in the Minimum Statistic...
متن کاملSpeech dereverberation using long short-term memory
Recently, neural networks have been used for not only phone recognition but also denoising and dereverberation. However, the conventional denoising deep autoencoder (DAE) based on the feed-forward structure is not capable of handling very long speech frames of reverberation. LSTM can be effectively trained to reduce the average error between the enhanced signal and the original clean signal by ...
متن کاملLong short-term memory networks for noise robust speech recognition
In this paper we introduce a novel hybrid model architecture for speech recognition and investigate its noise robustness on the Aurora 2 database. Our model is composed of a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net exploiting long-range context information for phoneme prediction and a Dynamic Bayesian Network (DBN) for decoding. The DBN is able to learn pronunciation va...
متن کاملthe effects of keyword and context methods on pronunciation and receptive/ productive vocabulary of low-intermediate iranian efl learners: short-term and long-term memory in focus
از گذشته تا کنون، تحقیقات بسیاری صورت گرفته است که همگی به گونه ای بر مثمر ثمر بودن استفاده از استراتژی های یادگیری لغت در یک زبان بیگانه اذعان داشته اند. این تحقیق به بررسی تاثیر دو روش مختلف آموزش واژگان انگلیسی (کلیدی و بافتی) بر تلفظ و دانش لغوی فراگیران ایرانی زیر متوسط زبان انگلیسی و بر ماندگاری آن در حافظه می پردازد. به این منظور، تعداد شصت نفر از زبان آموزان ایرانی هشت تا چهارده ساله با...
15 صفحه اولLong Short-term Memory
Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests. This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden s...
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 Signal Processing Letters
سال: 2019
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2019.2911879