A sawtooth waveform inspired pitch estimator for speech and music.

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Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation (2019, methodology): “orithm presented in [18]. The parameterization of the voice transformation algorithm is performed dynamically over time using as main control the F0 calculated using the swipe F0 estimation algorithm [19]. To achieve high quality formant shifting (or preservation), the order of the spectral envelope”
Crepe: A Convolutional Representation for Pitch Estimation (2018, methodology): “ implementation provided in mir eval [27] to compute the evaluation metrics. We compare CREPE against the current state of the art in monophonic pitch tracking, represented by the pYIN [13] and SWIPE [12] algorithms. To examine the noise robustness of each algorithm, we also evaluate their pitch tra”
A pitch extraction algorithm tuned for automatic speech recognition (2014, methodology): “We started our work by obtaining various off-the-shelf pitch extractors, namely Yin [2], Getf0 [3], SAcC [4], Wu [5], SWIPE [6] and YAAPT [7].”
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis (2018, unknown): “used for acoustic analysis and signal synthesis. The analysis produced a total of 259 acoustic features at 5 ms intervals. The features comprised linearly interpolated log pitch estimated using SWIPE [88], 60 mel-cepstrum features (MCEPs, with frequency warping 0.77 to approximate the Bark scale), a”