Recognition of emotion in music based on deep convolutional neural network

Sarkar, Rajib; Choudhury, Sombuddha; Dutta, Saikat; Roy, Aneek; Saha, Sanjoy Kumar

doi:10.1007/s11042-019-08192-x

Cited by 60 publications

(30 citation statements)

References 46 publications

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“…For largescale music signals, it is usually impossible to obtain the music detection result within the effective time. At the same time, they do not consider the interference of noise on the music detection results, and the robustness to noise is poor, which affects the classification and subsequent processing of music [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

2021

Complexity

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In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

2021

Complexity

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“…A network which takes in raw audio was proposed in [4], 1d convolution layers were primarily used in order to extract the relevant features from the audio. A VGGNet [2] inspired model was introduced in [17] which was able to achieve good results. They also introduced a run-length algorithm to compute a final emotion result for the whole signal after having predicted smaller segments of the audio.…”

Section: Automated Feature Extractionmentioning

confidence: 99%

Multi-Representational Music Emotion Recognition using Deep Convolution Neural Networks

Bathigama¹,

Madushika²

2021

Preprint

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“…You et al presented a method to classify music in an effective way based on vocalists [6]. In music information retrieval [8][9][10], authors employed music information retrieval to identify songs based on emotions. Patra et al [11] proposed an algorithm on mood taxonomy for songs based on lyrics and audio signals.…”

Section: Related Workmentioning

confidence: 99%