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Author: jsingh811

paper/paper.md

@@ -24,7 +24,9 @@ bibliography: paper.bib
 
 # Summary
 
-PyAudioProcessing is a Python based library for processing audio data, forming and extracting numerical features from audio and further building and testing machine learning models. This library allows you to extract features such as MFCC, GFCC, spectral features, chroma features and other beat based and cepstrum based features from audio to use with one's own classification backend or popular scikit-learn classifiers. This software contributes to the available open-source software by enabling users to use Python based machine learning backend with highly researched audio features such as GFCC and others that are actively researched for many applications but are not available in Python due to primary popularity and focus in MATLAB. This software aims to aid users in addressing research efforts using GFCC and other researched audio features possible with Python.
+PyAudioProcessing is a Python based library for processing audio data, constructing and extracting numerical features from audio, building and testing machine learning models and classifying data with existing pre-trained audio classification models or custom user-built models. It in an end-to-end solution for building features from raw audio samples and training a model that can then be used to classify unseen raw audio samples. This library allows the user to extract features such as MFCC, GFCC, spectral features, chroma features and other beat based and cepstrum based features from audio to use with one's own classification backend or popular scikit-learn classifiers that have been built into pyAudioProcessing. This software contributes to the available open-source software by enabling users to use Python based machine learning backend with highly researched audio features such as GFCC and others that are actively user for many audio classification based applications but are not readily available in Python due to primary popularity of research in MATLAB.  
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+This software aims to provide machine learning engineers, data scientists, researchers and students with a set of baseline models to classify audio, the ability to use this library to build features on custom training data, the ability to automatically train on a scikit-learn classifier and perform hyperparameter tuning using this library, the ability to export the built features for integration with any machine learning backend and the ability to classify audio files. This software furthers aims to aid users in addressing research efforts using GFCC and other evolving and actively researched audio features possible with Python.
 
 # Statement of need
 
@@ -36,7 +38,7 @@ Some other popular libraries for the domain of audio processing include librosa
 
 PyAudioProcessing adds multiple additional features. The library includes the implementation of GFCC features converted from MATLAB based research to allow users to leverage Python with features for speech classification and speaker identification tasks in addition to MFCC and spectral features that are useful for music and other audio classification tasks. It allows the user to choose from the different feature options and use single or combinations of different audio features. The features can be run through a variety of scikit-learn models including a grid search for best model and Hyperparameters, along with a final confusion matrix and cross validation performance statistics. It further allows for saving and exporting the different audio features per audio file for the user to be able to leverage those while using a different custom classifier backend that is not a part of scikit-learn's models. 
 
-The library further provides some pre-build audio classification models such as `speechVSmusic` classifier and `music genre` classifier for give the users a baseline of pre-trained models for their common audio classification tasks. The user can use the library to build custom classifiers with the help of the instructions in the README.
+The library further provides some pre-build audio classification models such as `speechVSmusic`, `speechVSmusicVSbirds` sound classifier and `music genre` classifier for give the users a baseline of pre-trained models for their common audio classification tasks. The user can use the library to build custom classifiers with the help of the instructions in the README.
 
 Given the use of this software in the community today inspires the need and growth of this software. It is referenced in a text book titled `Artificial Intelligence with Python Cookbook` published by Packt Publishing in October 2020 [@packt]. Additionally, pyAudioProcessing is a part of specific admissions requirement for a funded PhD project at University of Portsmouth <sup id="portsmouth">[1](#footnote_portsmouth)</sup>. It is further referenced in this thesis paer titled "Master Thesis AI Methodologies for Processing Acoustic Signals AI Usage for Processing Acoustic Signals" [@phdthesis].