Music emotion classification for Turkish songs using lyrics (original) (raw)
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Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion
International Journal of Electrical and Computer Engineering (IJECE), 2018
usic has lyrics and audio. That‟s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. „ooh‟, „ah‟, „yeah‟, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.
Music Emotion Recognition with Audio and Lyrics Features
Music Emotion Recognition (MER) is a field of science dedicated to recognizing emotions associated with music pieces. With the new interest in music therapy and music recommendation systems, MER has caught immense interest of scientists. This study is an attempt at discerning how well music related emotions can be predicted with music features; audio and lyrics. Emotion classes associated with songs were initially identified with clustering. Independent classification experiments were executed utilizing lyrics and audio features, to assess the comparative best model for predicting music emotions. The classification algorithms attempted in this research are Naïve Bayes, Random Forest, SVM and C4.5 decision. Random Forest with oversampling on the audio feature set produced comparative best results.
Emotion-based Analysis and Classification of Music Lyrics
2017
Music emotion recognition (MER) is gaining significant attention in the Music Information Retrieval (MIR) scientific community. In fact, the search of music through emotions is one of the main criteria utilized by users. Real-world music databases from sites like AllMusic or Last.fm grow larger and larger on a daily basis, which requires a tremendous amount of manual work for keeping them updated. Unfortunately, manually annotating music with emotion tags is normally a subjective process and an I would like to thank to the institution in which I work, Instituto Superior Miguel Torga (ISMT), for the help they gave me during this work. I would also like to thank to several colleagues xii and teachers from ISMT but also from CISUC. Personally, a work of this size and exigency, being a working student, was only possible with all the patience and love always demonstrated by the people to whom I dedicate this work: my wife Paula and my daughter Marta. I want to also highlight, for everything, my mother Adaltiva and my brother Flávio. I am also grateful to my close family and my friends, they know who they are.
We present a study on music emotion recognition from lyrics. We start from a dataset of 764 samples (audio+lyrics) and perform feature extraction using several natural language processing techniques. Our goal is to build classifiers for the different datasets, comparing different algorithms and using feature selection. The best results (44.2% F-measure) were attained with SVMs. We also perform a bi-modal analysis that combines the best feature sets of audio and lyrics.The combination of the best audio and lyrics features achieved better results than the best feature set from audio only (63.9% F-Measure against 62.4% F-Measure).
Classification and Regression of Music Lyrics: Emotionally-Significant Features
Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2016
This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams-baseline), adding other features, including novel features, improved the F-measure from 68.2%, 79.6% and 84.2% to 77.1%, 86.3% and 89.2%, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate between arousal hemispheres and valence meridians. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6% Fmeasure in the classification by quadrants. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.
Emotionally-Relevant Features for Classification and Regression of Music Lyrics
IEEE Transactions on Affective Computing, 2016
This research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams-baseline), adding other features, including novel features, improved the F-measure from 69.9%, 82.7% and 85.6% to 80.1%, 88.3% and 90%, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate each quadrant. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6% F-measure in the classification by quadrants. We also conducted experiments to identify interpretable rules that show the relation between features and emotions and the relation among features. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.
Evaluation of Music Classification Method based on Lyrics of English Songs
—Music is used for people practicing sports, for elderly individuals, and to help train the mind. Recently in music information science, studies have been conducted on music therapy and on music classification from a therapeutic point of view. However, most of these studies have classified music based on melody and tempo. No classification method that is based on lyrics has been performed for music therapy support. The authors previously propose a music classification method using emotional words included in lyrics toward music therapy support. As this method was developed for Japanese lyrics, it is necessary to evaluate the method for English lyrics. In this paper, the results of such evaluation is described. We also describe an improved method, appropriate for English lyrics.
Detecting and Classifying Emotion in Popular Music
Proceedings of the 9th Joint Conference on Information Sciences (JCIS), 2006
Music expresses emotion. However, analyzing the emotion in music by computer is a difficult task. Some work can be found in the literature, but the results are not satisfactory. In this paper, an emotion detection and classification system for pop music is presented. The system extracts feature values from the training music files by PsySound2 and generates a music model from the resulting feature dataset by a classification algorithm. The model is then used to detect the emotion perceived in music clips. To further improve the classification accuracy, we evaluate the significance of each music feature and remove the insignificant features. The system uses a database of 195 music clips to enhance reliability and robustness.
A Feature Survey for Emotion Classification of Western Popular Music
In this paper we propose a feature set for emotion classification of Western popular music. We show that by surveying a range of common feature extraction methods, a set of five features can model emotion with good accuracy. To evaluate the system we implement an independent feature evaluation paradigm aimed at testing the property of generalizability; the ability of a machine learning algorithm to maintain good performance over different data sets.
Music Genre and Emotion Recognition Using
2014
Gaussian Processes (GPs) are Bayesian nonparametric models that are becoming more and more popular for their superior capabilities to capture highly nonlinear data relationships in various tasks, such as dimensionality reduction, time series analysis, novelty detection, as well as classical regression and classification tasks. In this paper, we investigate the feasibility and applicability of GP models for music genre classification and music emotion estimation. These are two of the main tasks in the music information retrieval (MIR) field. So far, the support vector machine (SVM) has been the dominant model used in MIR systems. Like SVM, GP models are based on kernel functions and Gram matrices; but, in contrast, they produce truly probabilistic outputs with an explicit degree of prediction uncertainty. In addition, there exist algorithms for GP hyperparameter learning-something the SVM framework lacks. In this paper, we built two systems, one for music genre classification and another for music emotion estimation using both SVM and GP models, and compared their performances on two databases of similar size. In all cases, the music audio signal was processed in the same way, and the effects of different feature extraction methods and their various combinations were also investigated. The evaluation experiments clearly showed that in both music genre classification and music emotion estimation tasks the GP performed consistently better than the SVM. The GP achieved a 13.6% relative genre classification error reduction and up to an 11% absolute increase of the coefficient of determination in the emotion estimation task. INDEX TERMS Music genre classification, music emotion estimation, Gaussian processes. KONSTANTIN MARKOV (M'05) was born in Sofia, Bulgaria. He received the (Hons.