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Music Waveform Analysis Based on SOM Neural Network and Big Data.

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Summary

This summary is machine-generated.

This study introduces a novel algorithm using Self-Organizing Map (SOM) neural networks for accurate music waveform feature extraction and emotion analysis. The method enhances music information retrieval and understanding through advanced big data techniques.

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Area of Science:

Digital Signal Processing
Artificial Intelligence
Music Information Retrieval

Background:

Music waveform analysis is crucial for feature extraction in multimedia applications.
Deep learning advancements have spurred research into AI applications for music study.
Accurate extraction of musical features like amplitude and loudness is essential.

Purpose of the Study:

To design an effective method for extracting musical note features from audio signals.
To segment audio into bars and analyze music features within each segment.
To utilize Self-Organizing Map (SOM) neural networks for music emotion recognition.

Main Methods:

Utilized Fast Fourier Transform (FFT) and SOM neural networks for note extraction.
Employed window moving matching for audio segmentation into bars.
Improved Recurrent Neural Networks (RNNs) and applied SOM for emotion feature analysis.

Main Results:

The proposed SOM-based method accurately extracts music waveform features.
The algorithm enables more precise and rapid analysis of sound waveforms.
First-time application of SOM neural networks for analyzing music emotion models.

Conclusions:

The SOM neural network and big data approach effectively extracts and analyzes music waveform features.
This novel algorithm offers improved accuracy and speed in audio waveform analysis.
The study pioneers the use of SOM for music emotion recognition.