本文選題：樂曲識(shí)別 + 音符檢測��； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：樂曲自動(dòng)識(shí)別是新興的交叉學(xué)科,在音樂檢索領(lǐng)域和音樂自動(dòng)譜曲技術(shù)中有很重要的應(yīng)用價(jià)值。目前的樂曲識(shí)別研究熱點(diǎn)主要集中于單音符的識(shí)別,并且在識(shí)別精度、抗噪性能等方面存在著一定局限性。本文針對(duì)此問題,在深入了解音樂體系的基礎(chǔ)上,圍繞鋼琴樂曲識(shí)別中的諸多關(guān)鍵技術(shù),如多音符起止點(diǎn)檢測、幀基頻精確提取、音符基頻準(zhǔn)確計(jì)算等展開探討,為包含連續(xù)音符彈奏的鋼琴樂曲識(shí)別提出了解決方案,主要工作如下:一是基于單門限能量差法實(shí)現(xiàn)音樂段與噪聲段的分割,然后對(duì)音樂段基于短時(shí)能量差法進(jìn)行音符起止點(diǎn)檢測。上述方法利用了鋼琴的音樂特性來識(shí)別能量跳變點(diǎn),能夠有效提高音符起止點(diǎn)檢測準(zhǔn)確率,避免漏判、錯(cuò)判的情況。二是在研究自相關(guān)法、倒譜法與短時(shí)幅度差法等方法的基礎(chǔ)上,提出一種樂曲基頻提取的改進(jìn)方法,能夠突出幀樣本周期位置的峰值特性,從而避免了半頻、倍頻的影響,有效提高基頻提取的精度。三是在分析比較音樂信號(hào)波形起伏特性及音符幀樣本數(shù)據(jù)處理方法的基礎(chǔ)上,改進(jìn)了基頻計(jì)算方法,該方法賦予音符中間幀更高的權(quán)系數(shù),達(dá)到較傳統(tǒng)方法更高的精度和容錯(cuò)性。最后,綜合以上算法,搭建了一個(gè)基于安卓移動(dòng)終端+服務(wù)器的樂曲識(shí)別應(yīng)用系統(tǒng),對(duì)算法進(jìn)行測試,驗(yàn)證了算法的可行性和效率。
[Abstract]:Automatic music recognition is a new interdisciplinary subject, which has an important application value in the field of music retrieval and music automatic composing. The current research focus on music recognition is mainly focused on the recognition of single notes, and there are some limitations in recognition accuracy and noise resistance. On the basis of the music system, a number of key techniques in the recognition of piano music, such as the detection of multi note starting point and stop point, the accurate base frequency extraction of the frame and the accurate calculation of the basic frequency of the notes, are discussed. The main work is as follows: one is to realize the music segment based on the single threshold energy difference method. This method uses the musical characteristics of the piano to identify the energy jump points, which can effectively improve the accuracy of the detection of the starting and stop points, avoid the missing and misjudged cases. Two is in the study of autocorrelation, cepstrum and short-time amplitude difference. On the basis of the method, an improved method of fundamental frequency extraction of music is proposed, which can highlight the peak characteristic of the period position of the frame sample, thus avoid the influence of half frequency and frequency doubling, effectively improve the precision of the base frequency extraction. Three, on the basis of analyzing and comparing the wave characteristics of the music signal and the processing method of the sound frame sample data, the base of the analysis and comparison is improved. The method of frequency calculation gives a higher weight coefficient in the middle frame of the note, and achieves higher accuracy and fault tolerance than the traditional method. Finally, a music recognition application system based on Android mobile terminal + server is built, and the algorithm is tested, and the feasibility and efficiency of the algorithm are verified.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN912.3;TP311.56

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