Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them.However,very little research has been conducted in this domain.Hence,the aim of this study is to categorize soun...Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them.However,very little research has been conducted in this domain.Hence,the aim of this study is to categorize sounds generated in the environment so that the impairment individuals can distinguish the sound categories.To that end first we define nine sound classes--air conditioner,car horn,children playing,dog bark,drilling,engine idling,jackhammer,siren,and street music--typically exist in the environment.Then we record 100 sound samples from each category and extract features of each sound category using Mel-Frequency Cepstral Coefficients(MFCC).The training dataset is developed using this set of features together with the class variable;sound category.Sound classification is a complex task and hence,we use two Deep Learning techniques;Multi Layer Perceptron(MLP)and Convolution Neural Network(CNN)to train classification models.The models are tested using a separate test set and the performances of the models are evaluated using precision,recall and F1-score.The results show that the CNN model outperforms the MLP.However,the MLP also provided a decent accuracy in classifying unknown environmental sounds.展开更多
At present online shopping is very popular as it is very convenient for the customers.However,selecting smartphones from online shops is bit difficult only from the pictures and a short description about the item,and ...At present online shopping is very popular as it is very convenient for the customers.However,selecting smartphones from online shops is bit difficult only from the pictures and a short description about the item,and hence,the customers refer user reviews and star rating.Since user reviews are represented in human languages,sometimes the real semantic of the reviews and satisfaction of the customers are different than what the star rating shows.Also,reading all the reviews are not possible as typically,a smartphone gets thousands of reviews in popular online shopping platform like Amazon.Hence,this work aims to develop a recommended system for smartphones based on aspects of the phones such as screen size,resolution,camera quality,battery life etc.reviewed by users.To that end we apply hybrid approach,which includes three lexicon-based methods and three machine learning modals to analyze specific aspects of user reviews and classify the reviews into six categories--best,better,good or somewhat for positive comments and for negative comments bad or not recommended--.The lexicon-based tool called AFINN together with Random Forest prediction model provides the best classification F1-score 0.95.This system can be customized according to the required aspects of smartphones and the classification of reviews can be done accordingly.展开更多
文摘Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them.However,very little research has been conducted in this domain.Hence,the aim of this study is to categorize sounds generated in the environment so that the impairment individuals can distinguish the sound categories.To that end first we define nine sound classes--air conditioner,car horn,children playing,dog bark,drilling,engine idling,jackhammer,siren,and street music--typically exist in the environment.Then we record 100 sound samples from each category and extract features of each sound category using Mel-Frequency Cepstral Coefficients(MFCC).The training dataset is developed using this set of features together with the class variable;sound category.Sound classification is a complex task and hence,we use two Deep Learning techniques;Multi Layer Perceptron(MLP)and Convolution Neural Network(CNN)to train classification models.The models are tested using a separate test set and the performances of the models are evaluated using precision,recall and F1-score.The results show that the CNN model outperforms the MLP.However,the MLP also provided a decent accuracy in classifying unknown environmental sounds.
文摘At present online shopping is very popular as it is very convenient for the customers.However,selecting smartphones from online shops is bit difficult only from the pictures and a short description about the item,and hence,the customers refer user reviews and star rating.Since user reviews are represented in human languages,sometimes the real semantic of the reviews and satisfaction of the customers are different than what the star rating shows.Also,reading all the reviews are not possible as typically,a smartphone gets thousands of reviews in popular online shopping platform like Amazon.Hence,this work aims to develop a recommended system for smartphones based on aspects of the phones such as screen size,resolution,camera quality,battery life etc.reviewed by users.To that end we apply hybrid approach,which includes three lexicon-based methods and three machine learning modals to analyze specific aspects of user reviews and classify the reviews into six categories--best,better,good or somewhat for positive comments and for negative comments bad or not recommended--.The lexicon-based tool called AFINN together with Random Forest prediction model provides the best classification F1-score 0.95.This system can be customized according to the required aspects of smartphones and the classification of reviews can be done accordingly.
