Music Emotion Recognition Based on a Neural Network with an Inception-GRU Residual Structure

Han, Xiao; Chen, Fuyang; Jun-rong, Ban

doi:10.3390/electronics12040978

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…The proposed neural network was compared with recently proposed models, as represented in Table 4, including two types of preprocessed datasets. The inception-GRU Residual Structure method [12] achieved 84.23 % accuracy in music emotion classification on the Soundtrack dataset. Improved deep belief network [13] achieved 83.35% accuracy.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Notably, a novel approach utilizing an Inception-GRU residual structure has been put forth, capturing the intricacies of musical expressions with significant efficacy. This methodology, grounded in the spectral matrix derived from logarithmic short-time Fourier transform, has showcased promising results on the Soundtrack dataset, achieving an accuracy surpassing traditional machine learning models [12]. In this paper, the researches presented an optimized structure of the Inception-V1 model which combines different convolution layers in parallel, and a deeper matrix is formed by concatenation the results processed by the convolution layers.…”

Section: Literature Overviewmentioning

confidence: 99%

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Music emotion classification using a hybrid CNN-LSTM model

Yakovyna,

Korniienko

2023

AAIT

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The emotional content of music, interwoven with the intricacies of human affect, poses a unique challenge for computational recognition and classification. With the digitalization of music libraries expanding exponentially, there is a pressing need for precise, automated tools capable of navigating and categorizing vast musical repositories based on emotional contexts. This study advances music emotion classification in the field of music information retrieval by developing a deep learning model that accurately predicts emotional categories in music. The goal of this research is to advance the field of music emotion classification by leveraging the capabilities of convolutional neural networks combined with long short-term memory within deep learning frameworks. The contribution of this study is to provide a refined approach to music emotion classification, combining the power of convolutional neural networks and long short-term memory architectures with sophisticated preprocessing of the Emotify dataset for a deeper and more accurate analysis of musical emotions. The research introduces a novel architecture combining Convolutional Neural Networks and Long Short-Term Memory networks designed to capture the intricate emotional nuances in music. The model leverages convolutional neural networks for robust feature detection and Long Short-Term Memory networks for effective sequence learning, addressing the temporal dynamics of musical features. Utilizing the Emotify dataset, comprising tracks annotated with nine emotional features, the study expands the dataset by segmenting each track into 20 parts, thereby enriching the variety of emotional expressions. Techniques like the synthetic minority oversampling technique were implemented to counter dataset imbalance, ensuring equitable representation of various emotions. The spectral characteristics of the samples were analyzed using the Fast Fourier Transform, contributing to a more comprehensive understanding of the data. Through meticulous fine-tuning, including dropout implementation to prevent overfitting and learning rate adjustments, the developed model achieved a notable accuracy of 94.7 %. This high level of precision underscores the model's potential for application in digital music services, recommendation systems, and music therapy. Future enhancements to this music emotion classification system include expanding the dataset and refining the model architecture for even more nuanced emotional analysis

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Literature Overviewmentioning

confidence: 99%

Music emotion classification using a hybrid CNN-LSTM model

Yakovyna,

Korniienko

2023

AAIT

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“…In this paper, the Inception-BiGRU 28 module is built, as shown in Figure 4 . First, the logging data is reconstructed, and the two-dimensional data obtained by the reconstruction is used as the input of the network model.…”

Section: Methodsmentioning

confidence: 99%

The Bidirectional Gated Recurrent Unit Network Based on the Inception Module (Inception-BiGRU) Predicts the Missing Data by Well Logging Data

Sun

Zhang

et al. 2023

ACS Omega

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As a key bridge between logging and seismic data, acoustic (AC) logging data is of great significance for reservoir lithology, physical property analysis, and quantitative evaluation, and completing AC logging data can help to obtain high-resolution inversion profiles, which can provide a reliable basis for reservoir geological interpretation. However, in the actual mining process, the AC logging data is always missing due to instrument failure and borehole collapse in many areas, and re-logging is not only expensive but also difficult to achieve. However, the AC data can be completed by other obtained logging parameters. In this paper, a bidirectional gated recurrent unit network based on the Inception module is developed to complete the AC logging data. The Inception module extracts the logging data features and inputs the extracted logging data features into the bidirectional gated recurrent unit network, which can fully consider the characteristics of the current data and the data before and after the logging sequence to complete the missing AC logging data. Experimental results show that the hybrid model (Inception-BiGRU) has higher accuracy than traditional and widely used series forecasting models (gated recurrent unit network and long short-term memory network), and this method also provides a new idea for the completion of AC logging data.

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“…Chaudhary et al [22] use three stacked CNN layers to learn emotional features from music spectrograms. Han et al [23] use the idea of an Inception Module to extract features of different dimensions. Also, they use one-dimensional residual CNNs and Gate Recurrent Unit (GRU) to extract timing features.…”

Section: Deep Learning In Mermentioning

confidence: 99%

A Deep Attentive Network for Music Emotion Recognition using the chorus part of the music

Talaghat,

Parvinnia,

Boostani

et al. 2023

Preprint

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In the domain of music information retrieval, music emotion recognition (MER) is an active area of research. The chorus part of the music is the most critical and emotional section. In this paper, we proposed an MER approach that combines Convolutional Neural Networks with Long Short-Term Memory to extract emotional features from the log-mel spectrogram of the music chorus part. Then, the features feed into an attention mechanism block to enhance helpful information. Also, we introduce a new dataset for the MER task that consists of 10,438 samples with valence/arousal labels. The results show that using chorus parts of music can improve the deep models' performance. Also, experiments show our proposed method achieves 15% and 40% relative improvement of valence and arousal, respectively, on the R2 score compared to the state-of-the-art and baseline models.

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Music Emotion Recognition Based on a Neural Network with an Inception-GRU Residual Structure

Cited by 10 publications

References 28 publications

Music emotion classification using a hybrid CNN-LSTM model

Music emotion classification using a hybrid CNN-LSTM model

The Bidirectional Gated Recurrent Unit Network Based on the Inception Module (Inception-BiGRU) Predicts the Missing Data by Well Logging Data

A Deep Attentive Network for Music Emotion Recognition using the chorus part of the music

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