Feature selection and feature learning in arousal dimension of music emotion by using shrinkage methods

Zhang, Jiang Long; Huang, Xiang; Li, Yang; Xu, Ye; Sun, Shu Tao

doi:10.1007/s00530-015-0489-y

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…Audio feature extraction was performed with openS-MILE [46], a popular open-source library for audio feature extraction. Specifically, we used the "emobase" configuration file to extract a set of 988 low-level descriptors (LLDs) including MFCC, pitch, spectral, zero-crossing rate, loudness and intensity statistics, many of which have been shown to be effective for identifying emotion in music [38], [39], [47], [48]. Many other configurations are available in openSMILE but we provide the "emobase" set of acoustic features since it is well-documented and was designed for emotion recognition applications [49].…”

Section: Feature Extractionmentioning

confidence: 99%

Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

Chua¹,

Makris²,

Herremans³

et al. 2022

Preprint

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Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.

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Section: Feature Extractionmentioning

confidence: 99%

Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

Chua¹,

Makris²,

Herremans³

et al. 2022

Preprint

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show abstract

“…But for the researchers, it is challenging to get a strong idea about the process of MER because of the diversity and the complexity of the research systems. Hence, it is essential to compare and classify the techniques on the basis of text dependent and non-text dependent features utilised for the MER [13,14]. Some common issues arise when mapping the relationship between the emotions and music features.…”

Section: Introductionmentioning

confidence: 99%

An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model

Agarwal

2021

IET Signal Processing

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Music is the art of ‘language of emotions’. Recently, music mood recognition is an emerging task. An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression (SVR) model is developed for the music emotion recognition. Our main intention is to increase the accuracy of emotion classification of music by considering text‐dependent and non‐text‐dependent features. For the high level feature representation, stacked autoencoder is used with two hidden layers. Modified K‐Medoid‐based brain storm optimisation‐based support vector regression (SVR_KMBSO) model is utilised for the emotion classification. Using the K‐Medoid‐based brain storm algorithm, the optimal parameters of the SVR are selected. The proposed framework utilises ISMIR2012 dataset and NJU_V1 dataset for English and for Hindi; online songs are also gathered and used for the music mood recognition. All the three datasets include songs based on four emotions like happy, angry, relax and sad. The experimental results are evaluated and compared with the existing classifiers including SVR, deep belief network (DBN) and Recurrent neural network (RNN). The proposed method SVR_KMBSO achieved high accuracy using three different datasets.

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“…In the music emotion classification research of audio, Hwang et al [2] extracted 37 features to represent music samples, including rhythm, dynamics, and pitch, and utilized K-nearest neighbor classifier to output the results. Zhang et al [3] extracted 8 kinds of acoustic features to represent the arousal dimension in the 2D music emotion model and applied logistic regression methods to explain these features, but this research mainly focused on the 1D emotion and did not verify the specific emotion category. Ramani and Priya [4] extracted Mel frequency, spacing, and zero-crossing rate as separate representations of the best-fit ratio and used genetic algorithm as the classification technique.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Music Emotion Classification Method Based on Multifeature Combined Network Classifier

Chen

2020

Mathematical Problems in Engineering

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Aiming at the shortcomings of single network classification model, this paper applies CNN-LSTM (convolutional neural networks-long short-term memory) combined network in the field of music emotion classification and proposes a multifeature combined network classifier based on CNN-LSTM which combines 2D (two-dimensional) feature input through CNN-LSTM and 1D (single-dimensional) feature input through DNN (deep neural networks) to make up for the deficiencies of original single feature models. The model uses multiple convolution kernels in CNN for 2D feature extraction, BiLSTM (bidirectional LSTM) for serialization processing and is used, respectively, for audio and lyrics single-modal emotion classification output. In the audio feature extraction, music audio is finely divided and the human voice is separated to obtain pure background sound clips; the spectrogram and LLDs (Low Level Descriptors) are extracted therefrom. In the lyrics feature extraction, the chi-squared test vector and word embedding extracted by Word2vec are, respectively, used as the feature representation of the lyrics. Combining the two types of heterogeneous features selected by audio and lyrics through the classification model can improve the classification performance. In order to fuse the emotional information of the two modals of music audio and lyrics, this paper proposes a multimodal ensemble learning method based on stacking, which is different from existing feature-level and decision-level fusion methods, the method avoids information loss caused by direct dimensionality reduction, and the original features are converted into label results for fusion, effectively solving the problem of feature heterogeneity. Experiments on million song dataset show that the audio classification accuracy of the multifeature combined network classifier in this paper reaches 68%, and the lyrics classification accuracy reaches 74%. The average classification accuracy of the multimodal reaches 78%, which is significantly improved compared with the single-modal.

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Feature selection and feature learning in arousal dimension of music emotion by using shrinkage methods

Abstract: low-SONE), root mean square and loudness-flux. Moreover, the shrinkage methods apply in logistic regression perform better for classification than most of other methods. We get an average accuracy rate of 83.8 %.

Cited by 12 publications

References 26 publications

Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model

A Multimodal Music Emotion Classification Method Based on Multifeature Combined Network Classifier

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