Predicting Affect in Music Using Regression Methods on Low Level Features
نویسندگان
چکیده
Music has been shown to impact the affective states of the listener. The emotion in music task at the MediaEval challenge 2015 focuses on predicting the affective dimensions of valence and arousal in music using low level features. In particular, this edition of the challenge involves prediction on full length songs given a training set containing smaller 30 second clips. We approach the problem as a regression task and test several regression algorithms. We proposed these regression methods on the dataset from previous edition of the same task (Mediaeval 2014) involving prediction on 30 second clips instead of full length songs. Through evaluation on the 2015 data set, we obtain a point of reference for the model performances on longer song clips. Whereas our models perform relatively well in predicting arousal (root mean square error: .24), we do not obtain good results for valence prediction (root mean square error: .35). We analyze the results and the experimental setup and discuss plausible solutions for a better prediction.
منابع مشابه
Predicting Time-Varying Musical Emotion Distributions from Multi-Track Audio
Music exists primarily as a medium for the expression of emotions, but quantifying such emotional content empirically proves a very difficult task. Myriad features comprise emotion, and as such music theory provides no rigorous foundation for analysis (e.g. key, mode, tempo, harmony, timbre, and loudness all play some roll), and the weight of individual musical features may vary due to the expr...
متن کاملComparison of logistic regression and neural network models in predicting the outcome of biopsy in breast cancer from MRI findings
Background: We designed an algorithmic model based on the logistic regression analysis and a non-algorithmic model based on the Artificial Neural Network (ANN). Materials and methods: The ability of these models was compared together in clinical application to differentiate malignant from benign breast tumors in a study group of 161 patients' records. Each patient’s record consisted of 6 subjec...
متن کاملشناسایی خودکار سبک موسیقی
Nowadays, automatic analysis of music signals has gained a considerable importance due to the growing amount of music data found on the Web. Music genre classification is one of the interesting research areas in music information retrieval systems. In this paper several techniques were implemented and evaluated for music genre classification including feature extraction, feature selection and m...
متن کاملA Saliency Detection Model via Fusing Extracted Low-level and High-level Features from an Image
Saliency regions attract more human’s attention than other regions in an image. Low- level and high-level features are utilized in saliency region detection. Low-level features contain primitive information such as color or texture while high-level features usually consider visual systems. Recently, some salient region detection methods have been proposed based on only low-level features or hig...
متن کاملProviding a model for predicting blood pressure fluctuations after induction of general anesthesia with data mining: a brief report
Background: Fluctuations in blood pressure after induction of general anesthesia have played a significant role in complications of surgery. Therefore, the present study was performed by identifying the causes of blood pressure fluctuations after induction of anesthesia, predicting and preventing them. Methods: For this study which is a retrospective cohort, data mining methods in the data set...
متن کامل