<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a multi-layer feed-forward neural network is used to construct a Meier spectrogram recognition system. By analyzing the algorithmic role of recurrent neural, the backpropagation algorithm is applied to update the weights in the neural network to obtain the mapping relationship between audio input and output. Combined with the algorithmic formula of the spectrum, the short-time Fourier transform is used to analyze the audio information. By architecting a multilayer feedforward recurrent neural network, the music signals are fused and classified. The cross-entropy loss function is applied to calculate the accuracy of micro and macro averages to improve the accuracy of music signal feature recognition. The results show that the feedforward recurrent neural network has the lowest error rate in different note recognition, and the error rate for “do” is 4%.</p>
</abstract>

In this paper, a multi-layer feed-forward neural network is used to construct a Meier spectrogram recognition system. By analyzing the algorithmic role of recurrent neural, the backpropagation algorithm is applied to update the weights in the neural network to obtain the mapping relationship between audio input and output. Combined with the algorithmic formula of the spectrum, the short-time Fourier transform is used to analyze the audio information. By architecting a multilayer feedforward recurrent neural network, the music signals are fused and classified. The cross-entropy loss function is applied to calculate the accuracy of micro and macro averages to improve the accuracy of music signal feature recognition. The results show that the feedforward recurrent neural network has the lowest error rate in different note recognition, and the error rate for “do” is 4%.

Research on music signal feature recognition and reproduction technology based on multilayer feedforward neural network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a multi-layer feed-forward neural network is used to construct a Meier spectrogram recognition system. By analyzing the algorithmic role of...

Research on music signal feature recognition and reproduction technology based on multilayer feedforward neural network

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 25. Dez. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00647

Schlüsselwörter
Recurrent neural, Loss function, Back-propagation algorithm, Mel-spectrogram recognition

© 2023 Huanzi Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on music signal feature recognition and reproduction technology based on multilayer feedforward neural network

Online veröffentlicht: 15. Okt. 2023

Seitenbereich: -

Eingereicht: 25. Dez. 2022

Akzeptiert: 25. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00647

SchlüsselwörterRecurrent neural, Loss function, Back-propagation algorithm, Mel-spectrogram recognition

© 2023 Huanzi Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Recurrent neural, Loss function, Back-propagation algorithm, Mel-spectrogram recognition