Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance ...Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance for prediction.The publicly accessible Kaggle data consists of 92,755 rows and 20 columns.Using multiple model comparisons for regression and classification,this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy.This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song’s popularity using both classification and regression machine learning methods,with an emphasis on Indonesia based on consumer culture theory.展开更多
For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed...For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.展开更多
文摘Using regression and classification machine learning algorithms,this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia,and then evaluates the feature importance for prediction.The publicly accessible Kaggle data consists of 92,755 rows and 20 columns.Using multiple model comparisons for regression and classification,this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy.This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song’s popularity using both classification and regression machine learning methods,with an emphasis on Indonesia based on consumer culture theory.
文摘For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.
