FAC: A Music Recommendation Model Based on Fusing Audio and Chord Features (115)

Feng, Weite; Liu, Junrui; Li, Tong; Yang, Zhen; Wu, Di

doi:10.1142/s0218194022500577

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In terms of music recommendation, Feng et al [8] proposed to model and combine melody, chord, and rhythm features and utilized a multilayer perceptron for music recommendation. The experimental results indicated a 3.52% improvement over the best baseline.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Neural Network-based Automatic Music Multi-Instrument Classification Approach

Guo

2024

IJACSA

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The automatic classification of multi-instruments plays a crucial role in providing services for music retrieval and recommendation. This paper focuses on automatic multiinstrument classification. Firstly, instrument features were analyzed, and Mel-frequency cepstral coefficient (MFCC) and perceptual linear predictive coefficient (PLPC) were extracted from instrument signals. Features were selected using the entropy weight method. The optimal initial weight threshold of a back-propagation neural network (BPNN) was obtained by utilizing the sparrow search algorithm (SSA), achieving a SSA-BPNN classifier. Experiments were conducted using the IRMAS dataset. The results demonstrated that the combination of MFCC and PLPC selected through the entropy weight method achieved the best performance in automatic multi-instrument classification. The method yielded high P value, recall rate, and F1 value, 0.72, 0.71, and 0.71, respectively. Moreover, it outperformed other algorithms such as support vector machine and XGBoost. These results confirm the reliability of the automatic multi-instrument classification method proposed in this paper, making it suitable for practical applications.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Neural Network-based Automatic Music Multi-Instrument Classification Approach

Guo

2024

IJACSA

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“…The analysis and processing of music signals can provide support for tasks such as music information retrieval and music genre classification, making it a highly important research direction in the field of music [2]. Numerous methods have already been applied [3], such as deep learning (DL) [4], convolutional neural network [5], and deep neural network [6]. Li et al [7] designed a supervised robust non-negative matrix factorization method to enhance the separation performance of instrumental music signals, such as piano and trombone.…”

Section: Introductionmentioning

confidence: 99%

Improving piano music signal recognition through enhanced frequency domain analysis

Gao

2024

J. meas. eng.

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Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis.

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BLNN:a muscular and tall architecture for emotion prediction in music

2024

Soft Comput

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In order to perform emotion prediction in music quickly and accurately, we have proposed a muscular and tall neural network architecture for music emotion classification. Specifically, during the audio pre-processing stage, we converge mel-scale frequency cepstral coefficients features and residual phase features with weighting, enabling the extraction of more comprehensive music emotion characteristics. Additionally, to enhance the accuracy of predicting musical emotion while reducing computational complexity during training phase, we consolidate Long short term memory network with Broad learning system network. We employ long short term memory structure as the feature mapping node of broad learning system structure, leveraging the advantages of both network models. This novel Neural Network architecture, called BLNN (Broad-Long Neural Network), achieves higher prediction accuracy. i.e., 66.78%, than single network models and other benchmark with/without consolidation methods. Moreover, it achieves lower time complexity than other excellent models, i.e., 169.32 s of training time and 507.69 ms of inference time, and achieves the optimal balance between efficiency and performance. In short, the extensive experimental results demonstrate that the proposed BLNN architecture effectively predicts music emotion, surpassing other models in terms of accuracy while reducing computational demands. In addition, the detailed description of the related work, along with an analysis of its advantages and disadvantages, and its future prospects, can serve as a valuable reference for future researchers.

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FAC: A Music Recommendation Model Based on Fusing Audio and Chord Features (115)

Cited by 3 publications

References 16 publications

Research on Neural Network-based Automatic Music Multi-Instrument Classification Approach

Research on Neural Network-based Automatic Music Multi-Instrument Classification Approach

Improving piano music signal recognition through enhanced frequency domain analysis

BLNN:a muscular and tall architecture for emotion prediction in music

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