A method for classifying noise data of human crowd, the method comprising: capturing the noise data from one or more sources, wherein the noise data is associated with a plurality of speech signals; extracting features from the noise data by using one or more computation techniques, wherein the features comprises spectral domain features and time domain features; developing classification models for both the spectral domain features and the time domain features and combination of the spectral domain features and the time domain features, wherein the classification models comprises Support Vector Machines, SVM, classification models developed by using SVM technique, and wherein the SVM models comprises SVM model using spectral domain features, SVM model using time domain features and SVM model using combination of the spectral domain features and the time domain features; extracting a discriminative information with respect to the noise data by using the classification models, wherein the discriminative information recognizes the plurality of speech signals; computing a performance matrix for each of the classification models by using the discriminative information, wherein the performance matrix comprises one or more classified noise elements with respect to the noise data, wherein each classified noise element is associated with a classification performance score with respect to a spectral domain feature and a time domain feature, and wherein the classified noise elements provide the classification of the noise data; fusing the classification performance score of the one or more classifying elements with respect to each of the spectral domain features and the time domain features by summing a weighted performance score of the SVM model using spectral domain features and a weighted performance score of the SVM model using time domain features to obtain fused scores, wherein the weighted performance score of the SVM model using spectral domain features is obtained by applying a first weight Wi to the performance score of the SVM model using spectral domain features, wherein the weighted performance score of the SVM model using time domain features is obtained by applying a second weight W j to the performance score of the SVM model using time domain features, wherein the first weight Wi varies in steps of 0.1 from 0 to 1, and the second weight W j is determined using a formula, W j = 1 - W i, and wherein the fusion results in multiple combinations of scores of the spectral domain features and the time domain features with respect to the one or more classifying elements; and computing a score fused performance matrix by using the fused scores, wherein the score fused performance matrix comprises one or more classifying elements of the noise data with respect to the multiple combinations. capturing the noise data from one or more sources, wherein the noise data is associated with a plurality of speech signals; extracting features from the noise data by using one or more computation techniques, wherein the features comprises spectral domain features and time domain features; developing classification models for both the spectral domain features and the time domain features and combination of the spectral domain features and the time domain features, wherein the classification models comprises Support Vector Machines, SVM, classification models developed by using SVM technique, and wherein the SVM models comprises SVM model using spectral domain features, SVM model using time domain features and SVM model using combination of the spectral domain features and the time domain features; extracting a discriminative information with respect to the noise data by using the classification models, wherein the discriminative information recognizes the plurality of speech signals; computing a performance matrix for each of the classification models by using the discriminative information, wherein the performance matrix comprises one or more classified noise elements with respect to the noise data, wherein each classified noise element is associated with a classification performance score with respect to a spectral domain feature and a time domain feature, and wherein the classified noise elements provide the classification of the noise data; fusing the classification performance score of the one or more classifying elements with respect to each of the spectral domain features and the time domain features by summing a weighted performance score of the SVM model using spectral domain features and a weighted performance score of the SVM model using time domain features to obtain fused scores, wherein the weighted performance score of the SVM model using spectral domain features is obtained by applying a first weight Wi to the performance score of the SVM model using spectral domain features, wherein the weighted performance score of the SVM model using time domain features is obtained by applying a second weight W j to the performance score of the SVM model using time domain features, wherein the first weight Wi varies in steps of 0.1 from 0 to 1, and the second weight W j is determined using a formula, W j = 1 - W i, and wherein the fusion results in multiple combinations of scores of the spectral domain features and the time domain features with respect to the one or more classifying elements; and computing a score fused performance matrix by using the fused scores, wherein the score fused performance matrix comprises one or more classifying elements of the noise data with respect to the multiple combinations.