音樂曲風分類：以支持向量機與卷積神經網路分析音頻訊號

Music Genre Classification：Analyzing Audio Signals with Support Vector Machine and Convolutional Neural Network

陳柏宇(Po-Yu Chen)

指導教授 ： 陳景祥

共同指導教授 ： 鄧文舜(Wen-Shuenn Deng)

淡江大學/商管學院/大數據分析與商業智慧碩士學位學程/碩士(2019年)

https://doi.org/10.6846/TKU.2019.01017

摘要

當今數位音樂越來越流行，人們每天可以簡單地透過電腦或智慧型手機的音樂播放軟體等即時性地享受音樂，然而龐大的音樂檔案數量卻也同時對管理者在整理、歸納檔案上產生相當程度的困擾。本篇研究旨在運用當今熱門之機器學習與深度學習等資料分析方法協助管理者從大量的音樂資料中快速的有效找出符合特定曲風的音樂。
本篇研究以R語言實作分析，從音訊檔案讀取開始，針對音頻訊號以常見之梅爾頻率倒頻譜係數（MFCC）進行特徵萃取，完成後先運用隨機投影法之降維方法對資料進行維度縮減，最後再送入分類器執行建模分析，並同時比較資料降維前後對支持向量機（SVM）與卷積神經網路（CNN）預測成效之影響。本研究顯示，SVM在運算時間與分類預測正確率之表現皆優於深度學習CNN模型。另一方面，研究結果亦顯示隨機投影法在音頻資料上的維度縮減有不錯的表現。

關鍵字

梅爾頻率倒頻譜係數 ； 資料降維 ； 隨機投影法 ； 支持向量機 ； 卷積神經網路

並列摘要

Digital music is becoming more and more popular today. People can enjoy music instantly through music playing software in the computer or smart phone. However, the number of music files is often too large to organize or summarize. This research aims to use data analysis methods such as popular machine learning and deep learning to help managers quickly and effectively find out music that meets certain genres from a large number of music data.
This study uses R language to analyze the data. Feature extraction is performed on the audio signal importing with the common Mel-frequency Cepstral Coefficient (MFCC). After the importing, the music data is reduced by the dimensionality reduction method such as the Random Projection method, then finally sent to the classifier to perform modeling analysis. This study compare the impact of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) prediction before and after data reduction. We show that SVM performs better than the deep learning CNN model in terms of computation time and classification prediction accuracy. On the other hand, the results of the study also show that the random projection method for dimensional reduction on the audio data has a good performance.

並列關鍵字

MFCC ； Data dimensional reduction ； Random projection ； Support vector machine ； Convolutional neural network

參考文獻

參考文獻

一、英文文獻：

1. Z. Fu, G. Lu, K. Ting, and D. Zhang, “A survey of audio-based music classification and annotation” IEEE Transactions on Multimedia, vol. 13, pp. 303-319, Apr. 2011.

2. D. Chathuranga and L. Jayaratne, “Musical genre classification　using ensemble of　classifiers”, in IEEE Fourth Int. Conf. on　Computational Intelligence, Modelling and Simulation(CIMSim2012), (Kuantan, Pahang, Malaysia), pp. 237-242, Sep.25-27 2012.

3. H. Shih, Shrikanth S. Narayanan,“Automatic Main Melody Extraction from Midi Files with a Modified Lempel-Ziv Algorithm.”, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, (Hong Kong), pp. 9-12, 2001.