scholarly journals Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks

2013 ◽  
Author(s):  
Dimitri Palaz ◽  
Ronan Collobert ◽  
Mathew Magimai-Doss
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Speech Signal ◽  
Conditional Probabilities

scholarly journals Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Cevahir Parlak ◽  
Yusuf Altun
Keyword(s):  
Neural Networks ◽  
Fundamental Frequency ◽  
Convolutional Neural Networks ◽  
Speech Signal ◽  
Speech Signals ◽  
Pitch Detection ◽  
Frequency Detection ◽  
Temporal Smoothing ◽  
Telephone Speech ◽  
Musical Sounds

In this article, a novel pitch determination algorithm based on harmonic differences method (HDM) is proposed. Most of the algorithms today rely on autocorrelation, cepstrum, and lastly convolutional neural networks, and they have some limitations (small datasets, wideband or narrowband, musical sounds, temporal smoothing, etc.), accuracy, and speed problems. There are very rare works exploiting the spacing between the harmonics. HDM is designed for both wideband and exclusively narrowband (telephone) speech and tries to find the most repeating difference between the harmonics of speech signal. We use three vowel databases in our experiments, namely, Hillenbrand Vowel Database, Texas Vowel Database, and Vowels from the TIMIT corpus. We compare HDM with autocorrelation, cepstrum, YIN, YAAPT, CREPE, and FCN algorithms. Results show that harmonic differences are reliable and fast choice for robust pitch detection. Also, it is superior to others in most cases.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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