Main phases of the emotion prediction process [24]

Main phases of the emotion prediction process [24]

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The proven ability of music to transmit emotions provokes the increasing interest in the development of new algorithms for music emotion recognition (MER). In this work, we present an automatic system of emotional classification of music by implementing a neural network. This work is based on a previous implementation of a dimensional emotional pre...

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... Recently, there have been a body of works that applied deep neural network models to capture the association of mood/emotion and song by taking advantage of audio features (Saari et al. 2013;Panda 2019;Korzeniowski et al. 2020;Panda, Malheiro, and Paiva 2020;Medina, Beltrán, and Baldassarri 2020), lyrics features (Fell et al. 2019;Hrustanović, Kavšek, and Tkalčič 2021) as well as both lyrics and audio (Delbouys et al. 2018;Parisi et al. 2019;Wang, Syu, and Wongchaisuwat 2021;Bhattacharya and Kadambari 2018) features. Delbouys et al. classify mood of a song to either 'arousal' or 'valence' by utilizing a 100-dimensional word2vec embedding vector that is trained on 1.6 million lyrics in several different neural architectures such as GRU, LSTM, Convolutional Networks for their lyrics-based model. ...
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In this work, we study the association between song lyrics and mood through a data-driven analysis. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. We take advantage of state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. We find that a pretrained transformer-based language model in a zero-shot setting -- i.e., out of the box with no further training on our data -- is powerful for capturing song-mood associations. Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. Furthermore, by comparing the prediction of a model using lyrics with one using acoustic features, we observe that the relative importance of lyrics for mood prediction in comparison with acoustics depends on the specific mood. Finally, we verify if the models are capturing the same information about lyrics and acoustics as humans through an annotation task where we obtain human judgments of mood-song relevance based on lyrics and acoustics.
... By reviewing 10 articles on MER published in 2020 alone we have found 47 different low-level computational features being used separately or concatenated, to represent different aspects of the aforementioned highlevel features [3][4][5][6][7][8][9][10][11][12] . All these features are available offthe-shelf on Python libraries and MATLAB toolboxes, and 6 of them were found to be used on 76.6% of the publications reviewed: ...
... Spectral roll-off : relates to tone color and indicates the frequency below which approximately 85% of the magnitude spectrum distribution is concentrated [2]. Was used in [3], [5], [8][9][10], and [12]. ...
... Zero-crossing rate (ZCR): also relates to tone color and represents the number of times a waveform changes sign in a window, indicating change of frequency and noisiness [2]. Was used in [3], [5], [8][9][10], and [12]. ...
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Music is art, and art is a form of expression. Often , when a song is composed or performed, there may be an intent by the singer/songwriter of expressing some feeling or emotion through it, and, by the time the music gets in touch with an audience, a spectrum of emotional reactions can be provoked. For humans, matching the intended emotion in a musical composition or performance with the subjective perceptiveness of different listeners can be quite challenging, in account that this process is highly intertwined with people's life experiences and cognitive capacities. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which features are extracted to present this data to a model, that will train to predict the highest probability of an input matching a target. In this paper, we studied the most common features and models used in recent publications to tackle music emotion recognition, revealing which ones are best suited for songs (particularly acapella).
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... There are various models where at least two ragas have somewhat very similar or a comparative arrangement of notes yet are completely different in the melodic impact they produce due to factors like the gamaka, worldly sequencing (which needs to submit to the limitations introduced in the arohana and avarohana), just as spots of Swara accentuation and rest [9][10]. Furthermore, raga distinguishing proof is a procured ability that requires critical preparation and practice. ...
... Medina et al. [10] developed an emotional characterization of music utilizing neural organizations with the Medieval dataset. The current models utilized Multilayer Perceptron (MLP) which was prepared with the unreservedly accessible Medieval information base that was insufficient for procuring a decent arrangement in the results as the upsides of valence and excitement imbalanced order. ...
... • The membership functions of each class are a very important instrument in the field of emotions, considering that they facilitate the process of associating a value within a numerical scale, with a category; which is very useful for MER systems that initially apply a prediction model with a dimensional approach, and later require the adoption of a classification model with a categorical approach [23]. ...
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Chapter
The widespread availability of digital music on the internet has led to the development of intelligent tools for browsing and searching for music databases. Music emotion recognition (MER) is gaining significant attention nowadays in the scientific community. Emotion Analysis in music lyrics is analyzing a piece of text and determining the meaning or thought behind the songs. The focus of the paper is on Emotion Recognition from music lyrics through text processing. The fundamental concepts in emotion analysis from music lyrics (text) are described. An overview of emotion models, music features, and data sets used in different studies is given. The features of ANEW, a widely used corpus in emotion analysis, are highlighted and related to the music emotion analysis. A comprehensive review of some of the prominent work in emotion analysis from music lyrics is also included.