DOI: 10.5281/zenodo.3904388
title: A Replication Dataset for Fundamental Frequency Estimation

description: <em>Part of the dissertation Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods.<br> © 2020, Bastian Bechtold. All rights reserved.</em> Estimating the fundamental frequency of speech remains an active area of research, with varied applications in speech recognition, speaker identification, and speech compression. A vast number of algorithms for estimatimating this quantity have been proposed over the years, and a number of speech and noise corpora have been developed for evaluating their performance. The present dataset contains estimated fundamental frequency tracks of 25 algorithms, six speech corpora, two noise corpora, at nine signal-to-noise ratios between -20 and 20 dB SNR, as well as an additional evaluation of synthetic harmonic tone complexes in white noise. The dataset also contains pre-calculated performance measures both novel and traditional, in reference to each speech corpus’ ground truth, the algorithms’ own clean-speech estimate, and our own consensus truth. It can thus serve as the basis for a comparison study, or to replicate existing studies from a larger dataset, or as a reference for developing new fundamental frequency estimation algorithms. All source code and data is available to download, and entirely reproducible, albeit requiring about one year of processor-time. Included Code and Data <code>ground truth data.zip</code> is a JBOF dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora: CMU-ARCTIC (<em>consensus truth</em>) [1] FDA (<em>corpus truth</em> and <em>consensus truth</em>) [2] KEELE (<em>corpus truth</em> and <em>consensus truth</em>) [3] MOCHA-TIMIT (<em>consensus truth</em>) [4] PTDB-TUG (<em>corpus truth</em> and <em>consensus truth</em>) [5] TIMIT (<em>consensus truth</em>) [6] <code>noisy speech data.zip</code> is a JBOF datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora: NOISEX [7] QUT-NOISE [8] <code>synthetic speech data.zip</code> is a JBOF dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise. <code>noisy_speech.pkl</code> and <code>synthetic_speech.pkl</code> are pickled Pandas dataframes of performance metrics derived from the above data for the following list of fundamental frequency estimation algorithms: AUTOC [9] AMDF [10] BANA [11] CEP [12] CREPE [13] DIO [14] DNN [15] KALDI [16] MAPS MBSC [17] NLS [18] PEFAC [19] PRAAT [20] RAPT [21] SACC [22] SAFE [23] SHR [24] SIFT [25] SRH [26] STRAIGHT [27] SWIPE [28] YAAPT [29] YIN [30] <code>noisy speech evaluation.py</code> and <code>synthetic speech evaluation.py</code> are Python programs to calculate the above Pandas dataframes from the above JBOF datasets. They calculate the following performance measures: Gross Pitch Error (GPE), the percentage of pitches where the estimated pitch deviates from the true pitch by more than 20%. Fine Pitch Error (FPE), the mean error of grossly correct estimates. High/Low Octave Pitch Error (OPE), the percentage pitches that are GPEs and happens to be at an integer multiple of the true pitch. Gross Remaining Error (GRE), the percentage of pitches that are GPEs but not OPEs. Fine Remaining Bias (FRB), the median error of GREs. True Positive Rate (TPR), the percentage of true positive voicing estimates. False Positive Rate (FPR), the percentage of false positive voicing estimates. False Negative Rate (FNR), the percentage of false negative voicing estimates. F₁, the harmonic mean of precision and recall of the voicing decision. <code>Pipfile</code> is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs. 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