Analysis of Tibetan Folk Music Style Based on Audio Signal Processing

Ying, Ma; Li, Kaiyong; Hou, Jun Ming; Zangjia, Ga

doi:10.11648/j.jeee.20190706.13

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Numerous factors contribute to the difficulty of extracting musical elements, resulting in ineffective categorization and identification of musical genres. To enhance style recognition, researchers have taken a variety of approaches, including adding musical features, combining machine learning principles, support vector machine models, convolutional neural networks, and CRF models, or attempting to solve the problem using signal generation principles [4][5][6][7][8][9]. These approaches have improved the categorization of music to some degree, although feature extraction remains challenging in certain unique circumstances.…”

Section: Introductionmentioning

confidence: 99%

Research on Music Style Classification Based on Deep Learning

Wang

Sohail

2022

Computational and Mathematical Methods in Medicine

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Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

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Section: Introductionmentioning

confidence: 99%

Research on Music Style Classification Based on Deep Learning

Wang

Sohail

2022

Computational and Mathematical Methods in Medicine

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“…Audio is inevitably mixed with noise during the recording process. The core of the endpoint detection algorithm is to accurately identify the beginning and end of the music segment from the background noise [22]. At present, the most commonly used endpoint detection algorithm is the double-threshold method by signal time-domain characteristic parameters.…”

Section: Analysis Of Improved Endpoint Detection Algorithm Bymentioning

confidence: 99%

Piano Music Teaching under the Background of Artificial Intelligence

Huang

Ding

2022

Wireless Communications and Mobile Computing

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The work is aimed at solving the problems of easy trapping into local extremes and slow convergence speed of the traditional music teaching evaluation system on Backpropagation Neural Network (BPNN). The traditional note recognition methods are susceptible to high noise complexity. Firstly, the Levenberg Marquardt (LM) algorithm is used to optimize the BPNN; secondly, an improved endpoint detection algorithm is proposed by short-term energy difference, which can accurately identify the time value of each note in the piano playing audio. By the traditional frequency domain analysis method, a radical frequency extraction algorithm is proposed by the improved standard harmonic method, which can accurately identify the note’s pitch. Finally, a piano performance evaluation model by BPNN is implemented, and the model is implemented by the Musical Instrument Digital Interface (MIDI) system. This evaluation model can be used to correct the errors of students’ performances in the piano music teaching process and to perform overall evaluation, rhythm evaluation, and expressive evaluation. Teachers and students play minuet to collect experimental samples to train BPNN and test the performance of the evaluation model. The practical result shows that (1) after 3000 times of training, the neural network error is less than 0.01, and the network converges; (2) the evaluation results of the piano performance evaluation model designed are basically in line with the actual level of the performer and have specific feasibility; and (3) the optimized BPNN is used to correct errors during performances with an accuracy rate of 94.3%, which is 5.25% higher than the traditional method. The error correction accuracy rate for pitch is 92.9%, which is 5.21% higher than the traditional method. The optimized BPNN has significantly improved the error correction accuracy of the notes and pitches played by the player. The model can effectively help piano beginners correct errors and improve the accuracy and efficiency of the practice. The purpose of this study is to alleviate the scarcity of piano teachers, reduce the work intensity of piano teachers, realize automatic error correction and objective evaluation of playing, and provide necessary technical support for improving the efficiency of piano music teaching.

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“…FFT/DFT is an algorithm known as fast Fourier transform (FFT) computes a sequence's Discrete Fourier transform (DFT) or its inverse (IDFT) [6].In our research our all-music wav's sample rate is 44.1 kHz, window length is 512 which means 512 samples in each frame. After obtaining the signal's spectrum value, the energy spectrum is created by square rooting the result [7]. Mel filter banks are made up of m triangular filter banks that adhere to the Mel frequency scale.…”

Section: Wav Pre-processingmentioning

confidence: 99%

“…xt represents the input and ht-1 represents previous hidden states. Equation ( 6) is used to calculate the GRU state at the time interval t and the candidate state is calculated using Eq (7).…”

Section: = ( + ) (4)mentioning

confidence: 99%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. To extract meaningful knowledge, however, past studies' shortcomings of low accuracy and overfitting have to be addressed. We have proposed a model combining Conv1D, Bi-GRU and the Bahdanau attention mechanism for music emotion classification of our Bengali music dataset. The model integrates distinct MFCCs wav preprocessing methods with deep learning methods and attention-based methods. The attention mechanism has increased the accuracy of the proposed classification model. The music is finally classified into one of the four emotion classes: Angry, Happy, Relax, Sad. The proposed Conv1D+BiGRU+Attention model is validated as more effective and efficient at classifying emotions in the Bengali music dataset than baseline methods, according to comparisons with baseline models. For our Bengali music dataset, the performance of our proposed model is 95%.

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Analysis of Tibetan Folk Music Style Based on Audio Signal Processing

Cited by 3 publications

References 2 publications

Research on Music Style Classification Based on Deep Learning

Research on Music Style Classification Based on Deep Learning

Piano Music Teaching under the Background of Artificial Intelligence

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

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