本文選題：和弦識別　切入點：對數(shù)音級輪廓特征　出處：《天津大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)帶寬的增長,以及多媒體信息壓縮技術(shù)的不斷發(fā)展,互聯(lián)網(wǎng)上數(shù)字音樂的存儲和發(fā)布越來越普遍。為了應(yīng)對用戶隨時隨地檢索的需求,基于內(nèi)容的音樂檢索應(yīng)運而生。MIR中的中層特征就包括和弦,它包含了大量能夠表現(xiàn)音樂屬性的信息,對于分析音樂結(jié)構(gòu)和旋律方面具有非常重要的作用。因此,本文針對音樂和弦識別進(jìn)行了深入的研究,提出了魯棒性音樂和弦識別特征和兩種和弦估計方法。本文綜合應(yīng)用部分樂理、信號處理、模式識別等相關(guān)知識,提出了序列化稀疏表示分類和序列化支持向量機(jī)的和弦識別方法。其主要研究內(nèi)容是以信號處理為基礎(chǔ),從特征提取和和弦估計兩方面研究和弦識別。主要完成的工作包括以下幾個方面:(1)提出了魯棒性對數(shù)音級輪廓特征。和弦識別的一個關(guān)鍵是特征,在基于節(jié)拍的基礎(chǔ)上提出了LPCP,使得LPCP能夠更好地表達(dá)音頻內(nèi)容,提高和弦識別率;同時為了盡可能降低歌聲的影響,在計算PCP前,對音頻文件進(jìn)行歌聲伴奏分離,使得伴奏能夠更好地包含和弦特征,這樣音頻文件對和弦識別具有更好的魯棒性;(2)本文提出了基于序列化稀疏表示分類器的音樂和弦識別方法。在稀疏表示分類中,建立和弦樣本數(shù)據(jù)庫,對輸入的音頻片段進(jìn)行和弦估計。在此基礎(chǔ)上,結(jié)合隱形馬爾科夫鏈模型,克服需要大量訓(xùn)練得到模型參數(shù)的缺點,提出序列化稀疏表示模型。在對MIREX’09的數(shù)據(jù)庫中的大小和弦識別時,本論文提出的方法在使用本文的特征進(jìn)行識別時,識別率均高于目前的識別方法。(3)提出了序列化支持向量機(jī)的音樂和弦識別方法。為了克服稀疏表示分類時間較長的缺點,引入支持向量機(jī)用于和弦識別。該模型只需要提前訓(xùn)練好參數(shù),用于和弦估計時間較短。同時結(jié)合音樂和弦在時域上的變化特點,進(jìn)一步改進(jìn)支持向量機(jī),提出序列化支持向量機(jī)模型。
[Abstract]:With the growth of Internet bandwidth and the continuous development of multimedia information compression technology, the storage and distribution of digital music on the Internet is becoming more and more common. Content-Based Music Retrieval (CBIR) emerges as the times require. The middle level features of music retrieval include chords, which contain a large amount of information that can express the musical properties, and play a very important role in analyzing the music structure and melody. In this paper, the characteristics of robust music chord recognition and two kinds of chord estimation methods are proposed, and some related knowledge, such as music theory, signal processing, pattern recognition and so on, are synthetically applied in this paper. In this paper, a method of serialized sparse representation classification and serialization support vector machine is proposed, which is based on signal processing. This paper studies the recognition of chords from two aspects: feature extraction and chord estimation. The main work accomplished includes the following aspects: 1) the robust logarithmic tone level contour feature is proposed. One of the key points of chord recognition is the feature. In order to reduce the influence of singing as much as possible, LPCP is used to separate audio files before calculating PCP, which makes LPCP express audio content better and improve the recognition rate of chords. In this paper, a method of music chord recognition based on serialized sparse representation classifier is proposed. The chord sample database is established, and the input audio segment is estimated by the chord. On this basis, combined with the invisible Markov chain model, it overcomes the shortcoming that a lot of training is needed to obtain the model parameters. A serialized sparse representation model is proposed. When recognizing the size and chord in the MIREX'09 database, the method proposed in this paper uses the features of this paper to recognize. The recognition rate is higher than that of the current recognition method. (3) Serialization support vector machine (SVM) is proposed to recognize music and chord. In order to overcome the disadvantage of long time of sparse representation classification, the method of serialized support vector machine (SVM) is proposed. Support vector machine (SVM) is introduced for chord recognition. The model only needs to train the parameters in advance, and the estimation time of chord is short. At the same time, the support vector machine (SVM) is further improved according to the changing characteristics of music chord in time domain. A serialization support vector machine model is proposed.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN912.3

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1 饒中洋;音樂和弦識別的研究[D];天津大學(xué);2016年

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本文編號：1605750