SentiMozart: Music Generation based on Emotions

Madhok, Rishi; Goel, Shivali; Garg, Shweta

doi:10.5220/0006597705010506

Cited by 26 publications

(13 citation statements)

References 2 publications

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“…The software GhostWriter [13] used the Herman real‐time music generator to generate music with horror emotion colour. The research work of SentiMozart [14] generated the emotional music by recognising facial expressions. Davis and Mohammed [15] created the piano music with emotions through a rule‐based technique.…”

Section: Related Workmentioning

confidence: 99%

Learning to generate emotional music correlated with music structure features

Lin

Zhong

et al. 2022

Cognitive Comp and Systems

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Music can be regarded as an art of expressing inner feelings. However, most of the existing networks for music generation ignore the analysis of its emotional expression. In this paper, we propose to synthesise music according to the specified emotion, and also integrate the internal structural characteristics of music into the generation process. Specifically, we embed the emotional labels along with music structure features as the conditional input and then investigate the GRU network for generating emotional music. In addition to the generator, we also design a novel perceptually optimised emotion classification model which aims for promoting the generated music close to the emotion expression of real music. In order to validate the effectiveness of the proposed framework, both the subjective and objective experiments are conducted to verify that our method can produce emotional music correlated to the specified emotion and music structures.

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Section: Related Workmentioning

confidence: 99%

Learning to generate emotional music correlated with music structure features

Lin

Zhong

et al. 2022

Cognitive Comp and Systems

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“…10 shows the generated examples for each emotion. A set of generated MIDI examples can be found at link 6 . Figs.…”

Section: A Examples Of Generated Music Sequences With Provided Emotionmentioning

confidence: 99%

“…We notice the minor scale in the examples of Figs. 10b and 10c, which places them on the negative part of the valence 6 https://github.com/grekowj/musgenvae axis of Russell's model -emotions angry (e2) and sad (e3). In Figs.…”

Section: A Examples Of Generated Music Sequences With Provided Emotionmentioning

confidence: 99%

“…Deep learning techniques for music generation is a relatively new phenomenon [3] that enters the area of music composition, which is typically an area of human creativity and artistry. There are more and more music generating systems [4]- [6] that try to imitate human creativity, even compete with it, and learn from compositions created over the past centuries of human development. Music is an expression of human thoughts in the form of an organization of sounds in time.…”

Section: Introductionmentioning

confidence: 99%

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Monophonic Music Generation With a Given Emotion Using Conditional Variational Autoencoder

Grekow

Dimitrova-Grekow

2021

IEEE Access

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The rapid increase in the importance of human-machine interaction and the accelerating pace of life pose various challenges for the creators of digital environments. Continuous improvement of human-machine interaction requires precise modeling of the physical and emotional state of people. By implementing emotional intelligence in machines, robots are expected not only to recognize and track emotions when interacting with humans, but also to respond and behave appropriately. The machine should match its reaction to the mood of the user as precisely as possible. Music generation with a given emotion can be a good start to fulfilling such a requirement. This article presents the process of building a system generating music content of a specified emotion. As the emotion labels, four basic emotions: happy, angry, sad, relaxed, corresponding to the four quarters of Russell's model, were used. Conditional variational autoencoder using a recurrent neural network for sequence processing was used as a generative model. The obtained results in the form of the generated music examples with a specific emotion are convincing in their structure and sound. The generated examples were evaluated with two methods, in the first using metrics for comparison with the training set and in the second using expert annotation.

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“…[21] Machine Translation GRU Yao et al [22] Machine Translation Depth Gated LSTM Viswanadan et al [23] Machine Comprehension LSTM & GRU Jiao et al [24] Lexical Analysis BI-GRU Chen et al [25] Relation Extraction Bi-Tree-GRU Yao et al [26] NLP Analysis Improved LSTM Mohammed et.al. [27] Speaker, Language, and Gender Identification LSTM Speech/Audio/ Music Analysis & Synthesis Garcia et al [28] Music Generation LSTM & GRU Madhok et al [29] Music Generation Stacked LSTM Song et al [30] Music tagging GRU Xie e al. [31] Speech emotion classification LSTM Kang et al [32] Speech recognition LSTM Nakyama et al [33] Audio Chord classification LSTM & GRU Chen et al [34] Voice detection GRU Human Action/Interact ion Recognition Sho et al [35] Human interaction recognition Hierarchichal LSTM Sho et al [36] Person-Person Action Recognition Concurrent LSTSM…”

Section: Role Of Gated Rnns In Various Application Domainsmentioning

confidence: 99%

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

Kala¹,

Nandhini²

2020

KSII TIIS

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Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, moviewatching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

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SentiMozart: Music Generation based on Emotions

Cited by 26 publications

References 2 publications

Learning to generate emotional music correlated with music structure features

Learning to generate emotional music correlated with music structure features

Monophonic Music Generation With a Given Emotion Using Conditional Variational Autoencoder

Gated Recurrent Unit Architecture for Context-Aware Recommendations with improved Similarity Measures

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