通信与信息技术Issue(3)：1-4,4.
基于时频重排的梅尔频率倒谱系数分类算法研究
Research on Mel-frequency cepstral coefficients classification algorithm based on time-frequency reordering
摘要
Abstract
Mel frequency cepstral coefficients(MFCC)can effectively characterize heart murmurs by mimicking human auditory char-acteristics,but they cannot achieve a good time-frequency resolution.To address the issue that MFCC struggles to accurately capture and depict the subtle features of transient events,leading to limited representation capability for such events,a Mel spectrogram optimization al-gorithm for heart sound classification based on Convolutional Neural Networks(CNN)after time-frequency rearrangement is proposed.First,heart sounds are preprocessed through framing and denoising;then,Short-Time Fourier Transform(STFT)is applied to the preprocessed heart sound signals to rearrange their time-frequency domain,extracting the rearranged Mel spectrogram.Finally,the convolutional neural network is applied to the Yaseen dataset,which includes normal and four types of abnormal heart sounds,achieving a classification accuracy of 99.0%.The study shows that the proposed method of optimizing Mel spectrograms after time-frequency rearrangement can significantly improve the CNN's accuracy in classifying heart sound signals,providing new insights for intelligent auscultation of heart sounds.关键词
心脏病/时频重排/梅尔频率倒谱系数/心音/卷积神经网络Key words
Heart disease/Time-frequency rearrangement/Mel-frequency cepstral coefficient/Heart sound/Convolutional neural network分类
医药卫生引用本文复制引用
郭子健,丁贝贝,谢琰琰,张自娇,徐鹏飞..基于时频重排的梅尔频率倒谱系数分类算法研究[J].通信与信息技术,2026,(3):1-4,4.基金项目
郑州西亚斯学院2025年度教改基金资助项目(项目编号:2025JGYB15) （项目编号:2025JGYB15）
