Using Deep Neural Networks for Smoothing Pitch Profiles in Connected Speech

Ferro, Michele; Tamburini, Fabio

doi:10.4000/ijcol.476

ijcol

2019

DOI: 10.4000/ijcol.476

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Using Deep Neural Networks for Smoothing Pitch Profiles in Connected Speech

Michele Ferro¹,

Fabio Tamburini²

Abstract: This paper presents a new pitch tracking smoother based on deep neural networks (DNN). It leverages Long Short-Term Memories, a particular kind of recurrent neural network, for correcting pitch detection errors produced by state-of-the-art Pitch Detection Algorithms. The proposed system has been extensively tested using two reference benchmarks for English and exhibited very good performances in correcting pitch detection algorithms outputs when compared with the gold standard obtained with laryngographs.

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Cited by 2 publications

References 32 publications

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Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

Akdeniz,

Becerikli

2024

J Supercomput

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One of the most pressing challenges in audio forgery detection—a major topic of signal analysis and digital forensics research—is detecting copy-move forgery in audio data. Because audio data are used in numerous sectors, including security, but increasingly tampered with and manipulated, studies dedicated to detecting forgery and verifying voice data have intensified in recent years. In our study, 2189 fake audio files were produced from 2189 audio recordings on the TIMIT corpus, for a total of 4378 audio files. After the 4378 files were preprocessed to detect silent and unsilent regions in the signals, a Mel-frequency-based hybrid feature data set was obtained from the 4378 files. Next, RNN and LSTM deep learning models were applied to detect audio forgery in the data set in four experimental setups—two with RNN and two with LSTM—using the AdaGrad and AdaDelta optimizer algorithms to identify the optimum solution in the unlinear systems and minimize the loss rate. When the experimental results were compared, the accuracy rate of detecting forgery in the hybrid feature data was 76.03%, and the hybrid model, in which the features are used together, demonstrated high accuracy even with small batch sizes. This article thus reports the first-ever use of RNN and LSTM deep learning models to detect audio copy-move forgery. Moreover, because the proposed method does not require adjusting threshold values, the resulting system is more robust than other systems described in the literature.

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Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

Akdeniz,

Becerikli

2024

J Supercomput

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Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours

Faghih

Timoney

2022

Applied Sciences

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Pitch detection is usually one of the fundamental steps in audio signal processing. However, it is common for pitch detectors to estimate a portion of the fundamental frequencies incorrectly, especially in real-time environments and when applied to singing. Therefore, the estimated pitch contour usually has errors. To remove these errors, a contour smoother algorithm should be employed. However, because none of the current contour-smoother algorithms has been explicitly designed to be applied to contours generated from singing, they are often unsuitable for this purpose. Therefore, this article aims to introduce a new smoother algorithm that rectifies this. The proposed smoother algorithm is compared with 15 other smoother algorithms over approximately 2700 pitch contours. Four metrics were used for the comparison. According to all the metrics, the proposed algorithm could smooth the contours more accurately than other algorithms. A distinct conclusion is that smoother algorithms should be designed according to the contour type and the result’s final applications.

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Using Deep Neural Networks for Smoothing Pitch Profiles in Connected Speech

Cited by 2 publications

References 32 publications

Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

Recurrent neural network and long short-term memory models for audio copy-move forgery detection: a comprehensive study

Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours

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