计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
7期
179-182
,共4页
和弦识别%音级轮廓%节拍跟踪%音高频率倒谱系数%支持向量机
和絃識彆%音級輪廓%節拍跟蹤%音高頻率倒譜繫數%支持嚮量機
화현식별%음급륜곽%절박근종%음고빈솔도보계수%지지향량궤
chord recognition%Pitch Class Profile(PCP)%beat tracking%Pitch-frequency Cepstral Coefficients(PFCC)%Support Vector Machine(SVM)
和弦识别是自动音乐标注的基础，在歌曲翻唱识别、音乐分割及音频匹配等领域具有重要作用。针对不同乐器之间相同和弦识别率较低的问题，提出一种基于瞬时频率提取音级轮廓(PCP)特征的改进算法。该算法结合音高频率倒谱系数，将增强型PCP特征作为新的和弦识别特征，把音频信号输入到节拍跟踪器，依据动态规划算法提取信号的节拍信息，计算音频信号每一个节拍内的增强型PCP特征，采用结构化支持向量机分类方法实现对音乐和弦的识别。实验结果表明，与传统PCP特征相比，采用增强型PCP特征的和弦识别率提高了2.5%~6.7%。
和絃識彆是自動音樂標註的基礎，在歌麯翻唱識彆、音樂分割及音頻匹配等領域具有重要作用。針對不同樂器之間相同和絃識彆率較低的問題，提齣一種基于瞬時頻率提取音級輪廓(PCP)特徵的改進算法。該算法結閤音高頻率倒譜繫數，將增彊型PCP特徵作為新的和絃識彆特徵，把音頻信號輸入到節拍跟蹤器，依據動態規劃算法提取信號的節拍信息，計算音頻信號每一箇節拍內的增彊型PCP特徵，採用結構化支持嚮量機分類方法實現對音樂和絃的識彆。實驗結果錶明，與傳統PCP特徵相比，採用增彊型PCP特徵的和絃識彆率提高瞭2.5%~6.7%。
화현식별시자동음악표주적기출，재가곡번창식별、음악분할급음빈필배등영역구유중요작용。침대불동악기지간상동화현식별솔교저적문제，제출일충기우순시빈솔제취음급륜곽(PCP)특정적개진산법。해산법결합음고빈솔도보계수，장증강형PCP특정작위신적화현식별특정，파음빈신호수입도절박근종기，의거동태규화산법제취신호적절박신식，계산음빈신호매일개절박내적증강형PCP특정，채용결구화지지향량궤분류방법실현대음악화현적식별。실험결과표명，여전통PCP특정상비，채용증강형PCP특정적화현식별솔제고료2.5%~6.7%。
Chord recognition is the base of automatic music label, which plays an important role in the fields of song cover recognition, audio segmentation and audio matching etc. Among the different instruments, the recognition rate of the same chord is low. This paper proposes an improved chord recognition algorithm which combines the Pitch-frequency Cepstral Coefficients(PFCC) with Instantaneous-Frequency-based(IF) Pitch Class Profile(PCP) and uses the improved PCP as the new chord recognition feature. It inputs the audio signal into the beat tracker to extract the beat information of the signal which is based on dynamic programming algorithm, and calculates the improved PCP feature of the audio signal within each beat and realizes chord recognition by the structured Support Vector Machine(SVM). Results show that the ratios of chord recognition increases by 2.5%~6.7%after using the improved PCP than using the traditional PCP.
