This depository is for the related files used in the analysising and visualizing data within the project.

Dataset Description: Text Summarization Dataset

This dataset is designed for users aiming to train models for text summarization. It contains 2,225 rows of data with two columns: "Text" and "Summary". Each row features a detailed news article or piece of text paired with its corresponding summary, providing a rich resource for developing and fine-tuning summarization algorithms.

Key Features:

• Text: Full-length articles or passages that serve as the input for summarization.
• Summary: Concise summaries of the articles, which are ideal for training models to generate brief, coherent summaries from longer texts.

Future Enhancements:

This evolving dataset is planned to include additional features, such as text class labels, in future updates. These enhancements will provide more context and facilitate the development of models that can perform summarization across different categories of news content.

Usage:

Ideal for researchers and developers focused on text summarization tasks, this dataset enables the training of models to effectively compress information while retaining the essence of the original content.

Acknowledgment

We would like to extend our sincere gratitude to the dataset creator for their contribution to this valuable resource. This dataset, sourced from the BBC News Summary dataset on Kaggle, was created by Pariza. Their work has provided an invaluable asset for those working on text summarization tasks, and we appreciate their efforts in curating and sharing this data with the community.

Thank you for supporting research and development in the field of natural language processing!

File Description

This script processes and consolidates text data from various directories containing news articles and their corresponding summaries. It reads the files from specified folders, handles encoding issues, and then creates a DataFrame that is saved as a CSV file for further analysis.

Key Components:

1. Imports:

   • numpy (np): Numerical operations library, though it's not used in this script.
   • pandas (pd): Data manipulation and analysis library.
   • os: For interacting with the operating system, e.g., building file paths.
   • glob: For file pattern matching and retrieving file paths.

2. Function: get_texts

   • Parameters:
     • text_folders: List of folders containing news article text files.
     • text_list: List to store the content of text files.
     • summ_folder: List of folders containing summary text files.
     • sum_list: List to store the content of summary files.
     • encodings: List of encodings to try for reading files.
   • Purpose:
     • Reads text files from specified folders, handles different encodings, and appends the content to text_list and sum_list.
     • Returns the updated lists of texts and summaries.

3. Data Preparation:

   • text_folder: List of directories for news articles.
   • summ_folder: List of directories for summaries.
   • text_list and summ_list: Initialize empty lists to store the contents.
   • data_df: Empty DataFrame to store the final data.

4. Execution:

   • Calls get_texts function to populate text_list and summ_list.
   • Creates a DataFrame data_df with columns 'Text' and 'Summary'.
   • Saves data_df to a CSV file at /kaggle/working/bbc_news_data.csv.

5. Output:

   • Prints the first few entries of the DataFrame to verify the content.

Column Descriptions:

• Text: Contains the full-length articles or passages of news content. This column is used as the input for summarization models.
• Summary: Contains concise summaries of the corresponding articles in the "Text" column. This column is used as the target output for summarization models.

Usage:

• This script is designed to be run in a Kaggle environment where paths to text data are predefined.
• It is intended for preprocessing and saving text data from news articles and summaries for subsequent analysis or model training.

What is Pandas?

Pandas is a Python library used for working with data sets.

It has functions for analyzing, cleaning, exploring, and manipulating data.

The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

Why Use Pandas?

Pandas allows us to analyze big data and make conclusions based on statistical theories.

Pandas can clean messy data sets, and make them readable and relevant.

Relevant data is very important in data science.

What Can Pandas Do?

Pandas gives you answers about the data. Like:

Is there a correlation between two or more columns?

What is average value?

Max value?

Min value?

R script used with accompanying data frame 'plot_character' that is within the project to calculate summary statistics and structural equation modelling.

Dataset

This dataset was created by Amir Raja

Contents

PandasPlotBench

PandasPlotBench is a benchmark to assess the capability of models in writing the code for visualizations given the description of the Pandas DataFrame. 🛠️ Task. Given the plotting task and the description of a Pandas DataFrame, write the code to build a plot. The dataset is based on the MatPlotLib gallery. The paper can be found in arXiv: https://arxiv.org/abs/2412.02764v1. To score your model on this dataset, you can use the our GitHub repository. 📩 If you have… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/PandasPlotBench.

"WeAreHere!" Children's questionnaire. This dataset includes: (1) the WaH children's questionnaire (20 questions including 5-point Likert scale questions, dichotomous questions and an open space for comments). The Catalan version (original), and the Spanish and English versions of the questionnaire can be found in this dataset in pdf format. (2) The data frame in xlsx format, with the children's answers to the questionnaire (a total of 3664 answers) and a reduced version of it for doing the regression (with the 5-point likert scale variable "ask for help" transformed into a dichotomous variable). (3) The data frame in xlsx format, with the children's answers to the questionnaire and the categorization of their comments (sheet 1), the data frame with only the MCA variables selected (sheet 2), and the categories and subcategories table (sheet 3). (4) The data analysis procedure for the regression, the component and multiple component analysis (R script).

The dataset offers data on a number of variables related to Road Accident Severity in India, such as the time of day, the day of the week, the age range of drivers, gender, educational attainment, car attributes, driving history, road conditions, and the seriousness of accidents. We can learn more about the trends, connections, and possible risk factors associated with auto accidents by examining this dataset. The dataset offers valuable insights into the dynamics of road accidents, enabling authorities, policymakers, and researchers to make informed decisions regarding road safety measures and interventions.

stacked-summaries/onlystacked-xsum-1024

Same thing as stacked-summaries/stacked-xsum-1024 but filtered such that is_stacked=True. Please refer to the original dataset for info and to raise issues if needed. Basic info on train split:

0 document 116994 non-null string 1… See the full description on the dataset page: https://huggingface.co/datasets/stacked-summaries/onlystacked-xsum-1024.

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455:

Example data frame of class-level metrics.

Web archive derivatives of the University Archives collection from Columbia University Libraries. The derivatives were created with the Archives Unleashed Toolkit and Archives Unleashed Cloud. The cul-1914-parquet.tar.gz derivatives are in the Apache Parquet format, which is a columnar storage format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See this notebook for examples. Domains .webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc) Produces a DataFrame with the following columns: domain count Web Pages .webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content")) Produces a DataFrame with the following columns: crawl_date url mime_type_web_server mime_type_tika content Web Graph .webgraph() Produces a DataFrame with the following columns: crawl_date src dest anchor Image Links .imageLinks() Produces a DataFrame with the following columns: src image_url Binary Analysis Images PDFs Presentation program files Spreadsheets Text files Word processor files The cul-1914-auk.tar.gz derivatives are the standard set of web archive derivatives produced by the Archives Unleashed Cloud. Gephi file, which can be loaded into Gephi. It will have basic characteristics already computed and a basic layout. Raw Network file, which can also be loaded into Gephi. You will have to use that network program to lay it out yourself. Full text file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content. Domains count file. A text file containing the frequency count of domains captured within your web archive. Due to file size restrictions in Scholars Portal Dataverse, each of the derivative files needed to be split into 1G parts. These parts can be joined back together with cat. For example: cat cul-1914-parquet.tar.gz.part* > cul-1914-parquet.tar.gz

Derivatives of the Web Archive of Independent News Sites on Turkish Affairs collection from the Ivy Plus Libraries Confederation. The derivatives were created with the Archives Unleashed Toolkit and Archives Unleashed Cloud.

The ivy-12911-parquet.tar.gz derivatives are in the Apache Parquet format, which is a columnar storage format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See this notebook for examples.

Domains

.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)

Produces a DataFrame with the following columns:

• domain
• count

Web Pages

.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))

Produces a DataFrame with the following columns:

• crawl_date
• url
• mime_type_web_server
• mime_type_tika
• content

Web Graph

.webgraph()

Produces a DataFrame with the following columns:

• crawl_date
• src
• dest
• anchor

Image Links

.imageLinks()

Produces a DataFrame with the following columns:

• src
• image_url

Binary Analysis

• Audio
• Images
• PDFs
• Presentation program files
• Spreadsheets
• Text files
• Word processor files
  

The ivy-12911-auk.tar.gz derivatives are the standard set of web archive derivatives produced by the Archives Unleashed Cloud.

• Gephi file, which can be loaded into Gephi. It will have basic characteristics already computed and a basic layout.
• Raw Network file, which can also be loaded into Gephi. You will have to use that network program to lay it out yourself.
• Full text file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.
• Domains count file. A text file containing the frequency count of domains captured within your web archive.

Libraries Import:

Importing necessary libraries such as pandas, seaborn, matplotlib, scikit-learn's KMeans, and warnings. Data Loading and Exploration:

Reading a dataset named "Mall_Customers.csv" into a pandas DataFrame (df). Displaying the first few rows of the dataset using df.head(). Conducting univariate analysis by calculating descriptive statistics with df.describe(). Univariate Analysis:

Visualizing the distribution of the 'Annual Income (k$)' column using sns.distplot. Looping through selected columns ('Age', 'Annual Income (k$)', 'Spending Score (1-100)') and plotting individual distribution plots. Bivariate Analysis:

Creating a scatter plot for 'Annual Income (k$)' vs 'Spending Score (1-100)' using sns.scatterplot. Generating a pair plot for selected columns with gender differentiation using sns.pairplot. Gender-Based Analysis:

Grouping the data by 'Gender' and calculating the mean for selected columns. Computing the correlation matrix for the grouped data and visualizing it using a heatmap. Univariate Clustering:

Applying KMeans clustering with 3 clusters based on 'Annual Income (k$)' and adding the 'Income Cluster' column to the DataFrame. Plotting the elbow method to determine the optimal number of clusters. Bivariate Clustering:

Applying KMeans clustering with 5 clusters based on 'Annual Income (k$)' and 'Spending Score (1-100)' and adding the 'Spending and Income Cluster' column. Plotting the elbow method for bivariate clustering and visualizing the cluster centers on a scatter plot. Displaying a normalized cross-tabulation between 'Spending and Income Cluster' and 'Gender'. Multivariate Clustering:

Performing multivariate clustering by creating dummy variables, scaling selected columns, and applying KMeans clustering. Plotting the elbow method for multivariate clustering. Result Saving:

Saving the modified DataFrame with cluster information to a CSV file named "Result.csv". Saving the multivariate clustering plot as an image file ("Multivariate_figure.png").

GenBank data submission network R data frames by year from 1992-2018.

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455

Dataset

This dataset was created by Sohail K. Nikouzad

Contents

kaggle notebook
Github Repo

I found two datasets about converting text with context to pandas code on Hugging Face, but the challenge is in the context. The context in both datasets is different which reduces the results of the model. First let's mention the data I found and then show examples, solution and some other problems.

• Rahima411/text-to-pandas:

  • The data is divided into Train with 57.5k and Test with 19.2k.

  • The data has two columns as you can see in the example:

    • "Input": Contains the context and the question together, in the context it shows the metadata about the data frame.
    • "Pandas Query": Pandas code txt Input | Pandas Query -----------------------------------------------------------|------------------------------------------- Table Name: head (age (object), head_id (object)) | result = management['head.age'].unique() Table Name: management (head_id (object), | temporary_acting (object)) | What are the distinct ages of the heads who are acting? |

• hiltch/pandas-create-context:

  • It contains 17k rows with three columns:
    • question : text .
    • context : Code to create a data frame with column names, unlike the first data set which contains the name of the data frame, column names and data type.
    • answer : Pandas code.

      question           |            context             |       answer 
----------------------------------------|--------------------------------------------------------|---------------------------------------
What was the lowest # of total votes?  | df = pd.DataFrame(columns=['_number_of_total_votes']) | df['_number_of_total_votes'].min()   

As you can see, the problem with this data is that they are not similar as inputs and the structure of the context is different . My solution to this problem was: - Convert the first data set to become like the second in the context. I chose this because it is difficult to get the data type for the columns in the second data set. It was easy to convert the structure of the context from this shape Table Name: head (age (object), head_id (object)) to this head = pd.DataFrame(columns=['age','head_id']) through this code that I wrote. - Then separate the question from the context. This was easy because if you look at the data, you will find that the context always ends with "(" and then a blank and then the question. You will find all of this in this code. - You will also notice that more than one code or line can be returned to the context, and this has been engineered into the code. ```py def extract_table_creation(text:str)->(str,str): """ Extracts DataFrame creation statements and questions from the given text.

Args:
  text (str): The input text containing table definitions and questions.

Returns:
  tuple: A tuple containing a concatenated DataFrame creation string and a question.
"""
# Define patterns
table_pattern = r'Table Name: (\w+) \(([\w\s,()]+)\)'
column_pattern = r'(\w+)\s*\((object|int64|float64)\)'

# Find all table names and column definitions
matches = re.findall(table_pattern, text)

# Initialize a list to hold DataFrame creation statements
df_creations = []

for table_name, columns_str in matches:
  # Extract column names
  columns = re.findall(column_pattern, columns_str)
  column_names = [col[0] for col in columns]

  # Format DataFrame creation statement
  df_creation = f"{table_name} = pd.DataFrame(columns={column_names})"
  df_creations.append(df_creation)

# Concatenate all DataFrame creation statements
df_creation_concat = '

'.join(df_creations)

# Extract and clean the question
question = text[text.rindex(')')+1:].strip()

return df_creation_concat, question

After both datasets were similar in structure, they were merged into one set and divided into _72.8K_ train and _18.6K_ test. We analyzed this dataset and you can see it all through the **[`notebook`](https://www.kaggle.com/code/zeyadusf/text-2-pandas-t5#Exploratory-Data-Analysis(EDA))**, but we found some problems in the dataset as well, such as
> - `Answer` : `df['Id'].count()` has been repeated, but this is possible, so we do not need to dispense with these rows.
> - `Context` : We see that it contains `147` rows that do not contain any text. We will see Through the experiment if this will affect the results negatively or positively.
> - `Question` : It is ...

Dataframe used in a study on diabetes mortality in Peru.

Part of the dissertation Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods.© 2020, Bastian Bechtold. All rights reserved. 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

ground truth data.zip is a JBOF dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora:

CMU-ARCTIC (consensus truth) [1]FDA (corpus truth and consensus truth) [2]KEELE (corpus truth and consensus truth) [3]MOCHA-TIMIT (consensus truth) [4]PTDB-TUG (corpus truth and consensus truth) [5]TIMIT (consensus truth) [6]

noisy speech data.zip is a JBOF datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora:NOISEX [7]QUT-NOISE [8]

synthetic speech data.zip is a JBOF dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise.noisy_speech.pkl and synthetic_speech.pkl 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]MAPSMBSC [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]

noisy speech evaluation.py and synthetic speech evaluation.py 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.

Pipfile is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs.

The Python programs take about an hour to compute on a fast 2019 computer, and require at least 32 Gb of memory.References:

John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.