• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.115-121
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• 2022

In this paper, we propose a music classification system according to user emotions using Electroencephalogram (EEG) features that appear when listening to music. In the proposed system, the relationship between the emotional EEG features extracted from EEG signals and the auditory features extracted from music signals is learned through a deep regression neural network. The proposed system based on the regression model automatically generates EEG features mapped to the auditory characteristics of the input music, and automatically classifies music by applying these features to an attention-based deep neural network. The experimental results suggest the music classification accuracy of the proposed automatic music classification framework.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.363-372
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• 2013

When retrieving music with folksonomy tags, internal use of numeric tags (AV tags: tags consisting of Arousal and Valence values ) instead of word tags can partially solve the problem posed by synonyms. However, the two predecessor tasks should be done correctly; the first task is to map word tags to their numeric tags; the second is to get numeric tags of the music pieces to be retrieved. The first task is verified through our prior study and thus, in this paper, its significance is seen for the second task. To this end, we propose the music mapping table defining the relation between AV values and music and ANOVA tests are performed for analysis. The result shows that the arousal values and valence values of music have different distributions for 12 mood tags with or without synonymy and that their type I error values are P<0.001. Consequently, it is checked that the distribution of AV values is different according to music mood.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.43-52
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• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.

• Journal of information and communication convergence engineering
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• v.3 no.3
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• pp.115-118
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• 2005

In this study, the proposed algorithm is a correlativity signal in a mobile wireless channel that has estimated the direction of arrival. The proposed algorithm applied the space average method in a MUSIC algorithm. The diagonal matrix of the space average method was changed to inverse the matrix and to obtain a new signal correlation matrix. The existing algorithm was analyzed and compared by applying a proposed signal correlation matrix to estimate the direction of arrival in a MUSIC algorithm. The experiment resulted in a proposed algorithm with a min-norm method resolution at more than $5^{\circ}$. It improved more than $2^{\circ}$ in a MUSIC algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.27-30
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• 1997

The purpose of this study is to estimate human sensibility quantitatively under color and music stimulation and to examine the correlation between visual and auditory emotion. We measured biological signals such as EEG, ECG, skin conductance and the number of respiration in order to compare color with music sensibilities. Our result showed that red, yellow and violet color provoked active and exciting senses dominatively as dance, rock and blues music. While blue, cyan and pink color were involved in tranquil and resting emotions deeply as classic and ballade music. Our quantitative estimations of human sensibilites are useful in the design of manufactured goods.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.116-124
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• 2020

The advantage of the distance learning universities based on the 4th Industrial Revolution is that anyone can conveniently take lectures anytime, anywhere on the web. In addition, research has been actively conducted on the effect of light color and temperature control upon student performance during online classes. However, research on how the conditions of subjects, lighting colors, and music selection improve the degree of a student's concentration during online lectures has not been completed. To solve these problems in this paper, we have developed automatic analysis system SW for the weak subjects of learners by applying intelligent analysis algorithm, have proposed and simulated music therapy and art therapy. Moreover, It proposed in this paper an algorithm for an automatic analysis system, which shows the weak subjects of learners by adopting intelligence analysis algorithms. We also have presented and simulated a music therapy and art therapy algorithms, based on the blended learning, in order to increase students concentration during lecture.

• ETRI Journal
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• v.38 no.3
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• pp.510-517
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• 2016

Recent developments in the field of separation of mixed signals into music/voice components have attracted the attention of many researchers. Recently, iterative kernel back-fitting, also known as kernel additive modeling, was proposed to achieve good results for music/voice separation. To obtain minimum mean square error (MMSE) estimates of short-time Fourier transforms of sources, generalized spatial Wiener filtering (GW) is typically used. In this paper, we propose an advanced music/voice separation method that utilizes a generalized weighted ${\beta}$-order MMSE estimation (WbE) based on iterative kernel back-fitting (KBF). In the proposed method, WbE is used for the step of mixed music signal separation, while KBF permits kernel spectrogram model fitting at each iteration. Experimental results show that the proposed method achieves better separation performance than GW and existing Bayesian estimators.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1502-1508
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• 2017

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.143-146
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• 2017

In this paper, a novel approach is proposed to improve the performance of speech/music classification using the recurrent neural network (RNN) in the enhanced voice services (EVS) of 3GPP for hearing aids. Feature vectors applied to the RNN are selected from the relevant parameters of the EVS for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and large speech/music data. The proposed algorithm yields better results compared with the conventional scheme implemented in the EVS.