End-to-end music emotion variation detection using iteratively reconstructed deep features

Orjesek, Richard; Jarina, Roman; Chmulík, Michal

doi:10.1007/s11042-021-11584-7

Cited by 15 publications

(8 citation statements)

References 29 publications

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“…Among them, the blind separation of speech and music signals is a main research direction of blind source separation technology. A basic preprocessing method plays a very important role in the research of speech and music signal processing [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Gong

2022

Scientific Programming

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The integration and development of music curriculum signals have attracted the attention of researchers in real teaching scenarios. Based on the theory of multiple intelligences algorithm, this paper constructs a music curriculum integration and development model. This paper establishes a nonnegative matrix decomposition scheme, adds a constraint term that can reflect the smoothness of the time-varying gain matrix to the divergence-based objective function, and iteratively solves the problem of curriculum integration and development through the minimum optimization algorithm obtained by constructing the auxiliary function. In the process of simulation experiment, the optimal solution of each factor matrix was designed, the source music signal was reconstructed, and the sufficiently sparse source music signal was separated. The experimental results show that, compared with the parameter estimation without preprocessing, the parameter estimation with preprocessing is more accurate, the accuracy of the improved algorithm reaches 97.1%, the signal compression ratio reaches 0.656, and the enhanced signal can obtain at most 6 dB. The signal-to-noise ratio is improved, and the convergence speed is fast and asymptotically reaches the lower bound. It is suitable for parameter estimation of low-order and high-order autoregressive processes and effectively promotes the smooth development of music course signals.

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Section: Introductionmentioning

confidence: 99%

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Gong

2022

Scientific Programming

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“…In this way, all emotions can be represented using these two dimensions of V and A. The Lakh-Spotify Dataset [ 23 ] is one of the latest datasets that uses symbolic music paired with emotion labels in terms of VA. Valence and Arousal labels have also been used for tasks such as controlling emotion in generated music [ 24 , 25 , 26 , 27 ] as well as variation detection in emotion from music [ 28 ]. Due to the nature of the two representations, MER techniques for analyzing categorical annotations usually involve classification, while dimensional annotations require regression techniques.…”

Section: Related Workmentioning

confidence: 99%

MERP: A Music Dataset with Emotion Ratings and Raters’ Profile Information

Koh

Cheuk

Heung

et al. 2022

Sensors

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Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence-arousal space. Four additional songs were selected from the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. Their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.

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“…In general, both LSTM and GRU can use the gate function to retain the required data, so as to ensure that no information is lost. In addition, LSTM has a more complex structure and more parameters than GRU, so the overall learning rate of GRU is higher than that of LSTM [14].…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

Niu

2022

Mobile Information Systems

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A large number of music platforms have appeared on the Internet recently. The deep learning framework for music recommendation is still very limited when it comes to accurately identifying the emotional type of music and recommending it to users. Languages, musical styles, thematic scenes, and the ages to which they belong are all common classifications. And this is far from sufficient, posing difficulties in music classification and identification. As a result, this paper uses the methods of music emotion multi-feature extraction, BiGRU model design, and music theme scene classification model design to improve the accuracy of music emotion recognition. It develops the BiGRU emotion recognition model and compares it to other models. BiGRU can correctly identify happy and sad emotion music up to 79 percent and 81.01 percent of the time, respectively. It goes far beyond Rnet-LSTM, and the greater the difference in emotion categories, the easier it is to analyze the feature sequence containing emotional features and the higher the recognition accuracy. This is especially evident in the accuracy with which happiness and sadness are recognized. It can meet users' needs for music recognition in a variety of settings.

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End-to-end music emotion variation detection using iteratively reconstructed deep features

Cited by 15 publications

References 29 publications

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

MERP: A Music Dataset with Emotion Ratings and Raters’ Profile Information

Music Emotion Recognition Model Using Gated Recurrent Unit Networks and Multi-Feature Extraction

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