【摘要】：隨著因特網(wǎng)與網(wǎng)絡(luò)技術(shù)的快速發(fā)展,人們可以接觸到大量的在線音樂數(shù)據(jù),比如音樂原聲信號、歌詞、音樂曲風(fēng)或者內(nèi)容的分類以及其他網(wǎng)絡(luò)用戶的歌單等等。這種科技的進(jìn)步讓用戶在聽音樂時(shí)有了越來越多的樂趣,同時(shí),也對數(shù)據(jù)的處理提出了更高的要求,如何讓計(jì)算機(jī)更好地豐富用戶的音樂體驗(yàn)成為一個(gè)熱門問題,也促進(jìn)了音樂信息檢索領(lǐng)域的深入研究。音樂信息檢索,是一個(gè)跨學(xué)科的研究領(lǐng)域,涉及到音樂學(xué)、心理學(xué)、音樂學(xué)術(shù)研究、信號處理、機(jī)器學(xué)習(xí)等等。節(jié)拍跟蹤是音樂信息檢索的基礎(chǔ)問題之一。人們會不自主的跟隨音樂跺腳或者點(diǎn)頭的過程稱為節(jié)拍跟蹤,計(jì)算機(jī)的節(jié)拍跟蹤算法正是對人類這一感知過程的模擬。過去的二十多年中,節(jié)拍跟蹤研究領(lǐng)域已有大量深入的研究,也有越來越多的節(jié)拍跟蹤算法應(yīng)用于實(shí)際生活中。本文在認(rèn)真研究節(jié)拍跟蹤相關(guān)研究成果的基礎(chǔ)上,結(jié)合音樂基本理論與音頻信號技術(shù),提出一種基于最大最小距離法的節(jié)拍跟蹤算法,核心為起始節(jié)拍點(diǎn)的確定、BPM特征值提取和有效峰值提取三部分。本文的創(chuàng)新點(diǎn)在于將聚類算法應(yīng)用于節(jié)拍跟蹤研究,將峰值提取問題抽象為分類問題,從聚類的角度完成節(jié)拍序列的提取。具體研究步驟可概括為以下幾個(gè)方面:首先,對音樂信號進(jìn)行預(yù)處理,統(tǒng)一采樣頻率及幅度范圍。提取音樂信號的1-2s片段進(jìn)行時(shí)域處理,通過分析該片段的能量譜變化,確定起始節(jié)拍點(diǎn)。其次,對音樂信號進(jìn)行短時(shí)傅里葉變換得到頻譜,根據(jù)人類聽覺系統(tǒng)的感知特性,對頻譜幅度進(jìn)行對數(shù)處理,通過半波整流輸出端點(diǎn)強(qiáng)度曲線及其峰值的相位信息。根據(jù)端點(diǎn)強(qiáng)度曲線的自相關(guān)特性提取BPM特征值。最后,根據(jù)音樂的節(jié)拍和速度的關(guān)系以及周期信號的性質(zhì),利用最大最小距離法對端點(diǎn)強(qiáng)度曲線的峰值點(diǎn)進(jìn)行有效聚類,輸出節(jié)拍序列。本文采用MIREX2006測試數(shù)據(jù)進(jìn)行實(shí)驗(yàn),將本文的提出算法與MIREX2013節(jié)拍跟蹤比賽中性能較好的算法進(jìn)行對比。實(shí)驗(yàn)結(jié)果表明,本文提出的基于最大最小距離法節(jié)拍跟蹤算法對于不同曲風(fēng)、不同節(jié)奏類型的音樂信號,四項(xiàng)算法評估指標(biāo)P-Score、Cemgil、CMLc和AMLt的均值分別為57.35510、38.70537、17.15240和47.25912,能準(zhǔn)確有效地檢測出節(jié)拍序列,在全局正確性、連續(xù)正確率兩方面都有較大優(yōu)勢,綜合性能穩(wěn)定。
[Abstract]:With the rapid development of Internet and network technology, people can get access to a large number of online music data, such as music soundtrack, lyrics, music style or content classification, as well as other network users' song list and so on. The advances in technology have made it more and more fun for users to listen to music. At the same time, they have put forward higher requirements for the processing of data. How to make computers better enrich the music experience of users has become a hot issue. It also promotes the in-depth research in the field of music information retrieval. Music Information Retrieval is an interdisciplinary research field involving musicology, psychology, music academic research, signal processing, machine learning and so on. Beat tracking is one of the basic problems in music information retrieval. The process of stamping or nodding with music involuntarily is called rhythm tracking, and the computer beat tracking algorithm is the simulation of this process of human perception. In the past twenty years, there have been a lot of in-depth researches in the field of beat tracking, and more beat tracking algorithms have been applied in real life. In this paper, a beat tracking algorithm based on the maximum and minimum distance method is proposed, based on the research results of rhythm tracking, combined with the basic theory of music and audio signal technology. The core is the determination of the starting beat point and the extraction of the BPM eigenvalue and the effective peak value. The innovation of this paper lies in applying the clustering algorithm to the research of beat tracking, abstracting the peak extraction problem as a classification problem, and completing the extraction of beat sequences from the point of view of clustering. The specific research steps can be summarized as follows: firstly, preprocessing the music signal and unifying the sampling frequency and amplitude range. The 1-2s segment of music signal was extracted and processed in time domain, and the starting beat point was determined by analyzing the energy spectrum change of the segment. Secondly, the spectrum of music signal is obtained by short-time Fourier transform. According to the perception characteristics of human auditory system, the amplitude of spectrum is processed logarithmically, and the intensity curve of endpoints and the phase information of peak value are output by half-wave rectifier. The BPM eigenvalues are extracted according to the autocorrelation characteristics of the endpoint strength curve. Finally, according to the relationship between the rhythm and the speed of music and the property of the periodic signal, the maximum and minimum distance method is used to cluster the peak points of the endpoint intensity curve effectively, and the beat sequence is outputted. In this paper, the MIREX2006 test data are used to carry out the experiment, and the proposed algorithm is compared with the algorithm with better performance in the MIREX2013 beat tracking competition. The experimental results show that the proposed beat tracking algorithm based on the maximum and minimum distance method is suitable for different music signals with different styles and different rhythms. The average values of P-Scorex Cemgilg CMLc and AMLt are 57.3551010 ~ 38.70537 ~ 17.15240 and 47.25912 respectively, which can accurately and effectively detect the rhythm sequence, and have great advantages in both global correctness and continuous accuracy, and the comprehensive performance is stable.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7;TP18

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