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Front. Comput. Sci.    2022, Vol. 16 Issue (6) : 166335    https://doi.org/10.1007/s11704-021-0569-4
REVIEW ARTICLE
A survey of music emotion recognition
Donghong HAN1(), Yanru KONG1, Jiayi HAN2, Guoren WANG3
1. School of Computer Science and Engineering, Northeastern University, Shenyang 110000, China
2. Institute of Science and Technology Brain-Inspired Intelligence, Fudan University, Shanghai 200082, China
3. School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100089, China
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Abstract

Music is the language of emotions. In recent years, music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems, automatic music composing, psychotherapy, music visualization, and so on. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. This paper gives a detailed survey of music emotion recognition. Starting with some preliminary knowledge of music emotion recognition, this paper first introduces some commonly used evaluation metrics. Then a three-part research framework is put forward. Based on this three-part research framework, the knowledge and algorithms involved in each part are introduced with detailed analysis, including some commonly used datasets, emotion models, feature extraction, and emotion recognition algorithms. After that, the challenging problems and development trends of music emotion recognition technology are proposed, and finally, the whole paper is summarized.
Keywords artificial intelligence      deep learning      music emotion recognition     
Corresponding Author(s): Donghong HAN   
Just Accepted Date: 26 July 2021   Issue Date: 12 January 2022
 Cite this article:   
Donghong HAN,Yanru KONG,Jiayi HAN, et al. A survey of music emotion recognition[J]. Front. Comput. Sci., 2022, 16(6): 166335.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-021-0569-4
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I6/166335
Methodology Emotion conceptualization Description
Song-level MER Categorical approach Predict the categorical emotion labels of music pieces
Dimensional approach Predict the numerical emotion values of music pieces
MEVD Categorical approach Predict the dynamic categorical emotion variation within a music piece
Dimensional approach Predict the dynamic dimensional emotion variation within a music piece
Tab.1  MER research directions
Fig.1  MER framework
Model name Application domain Emotion conceptualization Number of classes/dimensions Emotional definition
Hevner affective ring [7] Music Categorical 67 Perceived
Russell’s circumplex model of affect [8,9] General Dimensional 2 Perceived
GEMS [10] Music Categorical 45 Induced
Thayer [11] General Dimensional 2 Perceived
Tab.2  A summary of emotion models
Dataset name Emotion conceptualization Number of songs Data type Genres Research directions
MediaEval emotion in music [19] Dimensional 1000 MP3 Rock, pop, soul, blues, etc. Dynamic
CAL500 [20] Categorical 500 MP3 ? Static
CAL500exp [21] Categorical 3223(segments) MP3 ? Dynamic
AMG1608[22] Dimensional 1608 WAV Rock, metal, country, jazz, etc. Static
DEAM [23] Dimensional 1802 MP3 Rock, pop, electronic, etc. Dynamic
MTurk [24] Dimensional 240 ? ? Dynamic
Soundtracks [25] Categorical and dimensional 360 MP3 Rap, R&B, electronic, etc. Static
Emotify music database [26] Categorical 400 MP3 Rock, classical, pop and electronic Static
Tab.3  A summary of datasets
Data format Preprocessing method Preprocessing tools Preprocessing result
WAV, MP3 Framing, windowing, MFCC extraction, spectrogram extraction, etc. Psysound (software), MIRtoolbox (MATLAB), Librosa (Python package), etc. MFCC, spectrogram, etc.
MIDI Main track extraction, etc. pretty_music, music21 (all Python package) Key, BPM, melody, etc.
Text Segmentation, cleaning, normalization, etc. NLTK, Gensim, Jieba, Stanford NLP (all Python package), etc. BOW, TF-IDF, word embeddings, etc.
Tab.4  Data format and processing information
First level audio feature Second level audio features
Rhythmic features Duration, pitch, energy, etc. [33]
Timbre features MFCC, zero crossing rate, chroma, etc. [32]
Spectral features Spectral flatness measure, spectral centroid, etc. [32]
Tab.5  Audio features
Reference Feature modalities Machine learning model Emotion model Dataset
[39] Lyric SVM 18 classes Self-built
[47] Physiological signals SVM, NB, KNN, DT 3 classes Peripheral physiological signals data
[49] Audio SVM 13/6 classes Self-built
[50] Audio and lyric SVM, RF 4 classes Self-built
[51] Audio and lyric SVM 4 classes Self-built
[52] Audio CLR 6/18 classes EMOTIONS, CAL500
Tab.6  Representative works of song-level categorical MER (ML)
Reference Feature modalities Machine learning model Emotion model Dataset
[13] Audio SVR, MLR VA model Self-built
[42] Lyric SVM VA model Self-built
[53] Audio AEG VA model MTurk, MER60
[54] Audio AEG VA model AMG1608
[55] Audio LR VA model AMG240
[56] Audio GPR VA model MediaEval emotion in music
[57] Audio SVR, MLR, GPR VA model Self-built
Tab.7  Representative works of song-level dimensional MER (ML)
Reference Feature modalities Machine learning model Emotion model Dataset
[13] Audio SVR VA model Self-built
[57] Audio SVR, MLR, GPR VA model Self-built
[58] Audio GMM 4 classes Self-built
[59] Audio SVM, SVR VA model Self-built
[60] Audio DS-SVR VA model MediaEval emotion in music
Tab.8  Representative works of MEVD (ML)
Reference Year Feature modalities Learning model Emotion model Dataset
[61] 2016 Audio and lyric DBM 4 classes MSD
[32] 2017 Audio CNN 18 classes CAL500, CAL500exp
[62] 2019 EEG CNN 2 classes EEG data collected from subjects
[63] 2020 Audio VGGNet 4 classes Soundtracks, Bi-Modal
[64] 2020 Audio CNN 2 classes EmoMusic
Tab.9  Representative works of song-level categorical MER (DL)
Reference Year Feature modalities Learning model Emotion model Dataset
[65] 2017 Audio LSTM, attention mechanism VA model Emotion in Music task at MediaEval 2015
[18] 2018 Audio BiLSTM Based on VA model DEAM
[66] 2018 Audio GARN GEMS Emotify music database
[67] 2018 Audio and lyric CNN, RNN, etc. VA model MSD
[68] 2019 Audio BCRSN Based on VA model DEAM, MTurk
[69] 2019 Audio VGG-based 8 dimensions Mid-level Perceptual Features dataset, Soundtracks
Tab.10  Representative works of song-level dimensional MER (DL)
Reference Year Feature modalities Learning model Emotion model Dataset
[12] 2014 Audio LSTM, SVR VA model MediaEval Emotion in Music
[57] 2014 Audio SVR, MLR, GPR VA model Self-built
[70] 2016 Audio DBLSTM VA model MediaEval Emotion in Music
[71] 2020 Audio Attentive LSTM VA model MediaEval Emotion in Music
Tab.11  Representative works of dimensional MEVD (DL)
Reference Method Dataset Performance
[61] Bi-modal deep boltzmann machine MSD 78.5% (Accuracy)
[67] CNN, LSTM MSD 0.219 for valence, 0.232 for arousal (R2)
[65] MCA MediaEval dataset 0.291 for valence, 0.241 for arousal (RMSE)
[70] DBLSTM MediaEval dataset 0.285 for valence, 0.225 for arousal (RMSE)
[32] CNN CAL500 42.6% (Marco average precision)
[52] CLR CAL500 48.8% (Marco average precision)
Tab.12  Metrics on some common datasets
Year Method Accuracy/%
2020 Mel spectrogram + CNN 69.5
2019 - 68
2018 STFT + CNN 61.17
2017 Mel spectrogram + DCNN+SVM 69.83
2016 FFT, MFCC + CNN 63.33
2015 - 66.17
2014 MFCC + SVM 66.33
2013 Visual and acoustic features + SVM 68.33
2012 Audio features + SVM based models 67.83
2011 Audio features + SRC 69.5
Tab.13  Results of AMC task in MIREX
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