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.

scholarly journals Fundamental Frequency Extraction of Noisy Speech Signals

2015 ◽  
Vol 43 ◽  
pp. 51-61
Author(s):  
Mirza A.F.M. Rashidul Hasan ◽  
Rubaiyat Yasmin ◽  
Dipankar Das ◽  
M. M. Hoque ◽  
M. I. Pramanik ◽  
...  
Keyword(s):  
Fundamental Frequency ◽  
Autocorrelation Function ◽  
Time Domain ◽  
Speech Signals ◽  
Original Signal ◽  
New Approach ◽  
Noisy Speech ◽  
Frequency Detection ◽  
Comparative Results

In this paper, we proposed a correlation based method which is a new approach using the autocorrelation function is weighted by the reciprocal of the YIN and very useful for accurate fundamental frequency extraction. The autocorrelation function and also YIN is a popular measurement in estimating fundamental frequency in time domain. In our proposed method, instead of the original signal, we employ its center clipping signal for obtaining the autocorrelation function and this function is weighted by the reciprocal of the YIN for fundamental frequency detection. Comparative results on female and male voices in white and exhibition noise shows that the proposed method can detect fundamental frequency with better accuracy in terms of gross pitch errors as compared to other related methods.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions
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