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Error correction in pitch detection using a deep learning based classification

Khadem Hosseini, M ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TASLP.2020.2977472
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. While pitch detection has been the focal subject of numerous research efforts for several decades, it is still a challenging task in noisy conditions. In this article, we propose a method to improve the pitch detection accuracy of conventional pitch detection methods. The proposed pitch detection process starts with using the pitch value estimated by a conventional pitch detection method. Then, it extracts pitch candidates according to the most probable types of errors in the initial estimation of high-pitch and low-pitch frames classified by a Deep Convolutional Neural Network (DCNN). Next, a restrained selection procedure is run to find the true pitch value from the set of pitch candidates. In this procedure, we employ two features (harmonic summation and Euclidian deviation), the soft decision of the DCNN, the pitch smoothness feature in successive frames, and the effect of the initial estimation in a cost function. The pitch value which leads to the lowest cost value is chosen as the estimated pitch value. The simulations on CSTR and KEELE databases, in noisy environments with twelve types of noise, were performed. The results show the superiority of the proposed method over the state-of-the-art methods under different SNR conditions. © 2014 IEEE
  6. Keywords:
  7. Pitch detection ; Convolution ; Convolutional neural networks ; Cost estimating ; Cost functions ; Deep neural networks ; Error correction ; Signal to noise ratio ; Harmonic summations ; Initial estimation ; Noisy conditions ; Noisy environment ; Research efforts ; Selection procedures ; State-of-the-art methods ; Deep learning
  8. Source: IEEE/ACM Transactions on Audio Speech and Language Processing ; Volume 28 , 2020 , Pages 990-999
  9. URL: https://ieeexplore.ieee.org/abstract/document/9019597