Merge pull request #38 from jsingh811/typo-fix

Fix typo

Author: jsingh811

paper/paper.md

@@ -40,7 +40,7 @@ PyAudioProcessing is a Python based library for processing audio data, construct
 
 5. Includes pre-trained models to provide users with baseline audio classifiers.
 
-It in an end-to-end solution for converting between audio file formats, building features from raw audio samples and training a machine learning 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. 
+It is an end-to-end solution for converting between audio file formats, building features from raw audio samples and training a machine learning 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. 
 
 MATLAB is the language of choice for a vast amount of research in the audio and speech processing domain. On the contrary, Python remains the language of choice for a vast majority of Machine Learning research and functionality. This library contains features converted to Python that were originally built in MATLAB following a research invention. 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.