發(fā)布時間：2018-02-28 14:09

  本文關(guān)鍵詞： 神經(jīng)網(wǎng)絡(luò) 指數(shù)平滑法 ARIMA模型 音樂 預(yù)測　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：音樂的流行趨勢可以根據(jù)當(dāng)前的流行藝人表現(xiàn)出來,因此對音樂流行趨勢的預(yù)測也就是對哪些音樂藝人即將成為未來一段時間內(nèi)的流行藝人的預(yù)測。而判斷某個藝人是否是流行藝人則可以根據(jù)該藝人最近一段時間里的音樂試聽量來判斷。通過統(tǒng)計分析用戶對音樂的操作(試聽、下載、收藏)記錄,預(yù)測出藝人在下一階段內(nèi)的音樂試聽量,從而可以判斷出哪些藝人在未來一段時間內(nèi)音樂試聽量最高,這些藝人即代表著未來一段時間內(nèi)的音樂流行趨勢。本文通過統(tǒng)計分析電子音樂平臺產(chǎn)生的用戶試聽、下載、收藏歌曲的行為記錄,結(jié)合二次指數(shù)平滑法、自回歸移動平均模型以及BP神經(jīng)網(wǎng)絡(luò)模型對藝人歌曲試聽量進行了預(yù)測,同時設(shè)計并實現(xiàn)了基于BP神經(jīng)網(wǎng)絡(luò)算法的音樂流行趨勢預(yù)測系統(tǒng)。本文的主要研究工作如下:1.通過閱讀大量國內(nèi)外文獻,研究了國內(nèi)外音樂試聽量預(yù)測的研究現(xiàn)狀、神經(jīng)網(wǎng)絡(luò)算法研究的現(xiàn)狀、該算法的特點以及在多個應(yīng)用領(lǐng)域中的使用情況。重點研究了BP神經(jīng)網(wǎng)絡(luò)算法的應(yīng)用,并對電子音樂平臺上產(chǎn)生的基礎(chǔ)數(shù)據(jù)進行統(tǒng)計分析,尋找影響藝人音樂試聽量的主要因素,最后使用BP神經(jīng)網(wǎng)絡(luò)算法對藝人在接下來一個月內(nèi)每天的音樂試聽總量進行了預(yù)測。2.在使用BP神經(jīng)網(wǎng)絡(luò)算法對藝人音樂試聽量進行預(yù)測的同時,使用了二次指數(shù)平滑法、自回歸移動平均模型對藝人音樂試聽量進行預(yù)測,最后對比三種預(yù)測方法的預(yù)測結(jié)果。3.設(shè)計并實現(xiàn)了基于BP神經(jīng)網(wǎng)絡(luò)算法的音樂流行趨勢預(yù)測系統(tǒng)。該系統(tǒng)是通過J2EE平臺,結(jié)合web開發(fā)技術(shù)、數(shù)據(jù)庫技術(shù)、數(shù)據(jù)挖掘技術(shù)進行開發(fā)的�？梢宰尣欢沃笖�(shù)平滑法、自回歸移動平均模型、BP神經(jīng)網(wǎng)絡(luò)算法等預(yù)測算法的工作人員也能通過操此系統(tǒng)預(yù)測藝人在下一階段中的音樂試聽量,從而判斷出哪些藝人即將代表下一階段的音樂流行趨勢。最后總結(jié)了本文的研究內(nèi)容,并對下一步的工作作出展望。
[Abstract]:The pop trend of music can be expressed according to the current pop artists. Therefore, the prediction of pop trends is a prediction of which musical artists will become pop artists for some time to come. And judging whether an artist is a pop artist can be based on the latest period of time. Through the statistical analysis of the user's operation of the music (listen to, listen to, Download, collect) records to predict the amount of music auditions that artists will be listening to in the next stage, so as to determine which artists will have the highest amount of music auditions over the next period of time. These artists represent the trend of music popularity for a period of time in the future. This paper, through statistical analysis of the users' listening, downloading and collecting songs generated by the electronic music platform, combines the quadratic exponential smoothing method. The autoregressive moving average model and BP neural network model were used to predict the audition quantity of artist songs. The main research work of this paper is as follows: 1. By reading a large number of domestic and foreign literature, the research status of music audition prediction at home and abroad is studied. The present situation of neural network algorithm research, the characteristics of the algorithm and its application in many application fields are discussed. The application of BP neural network algorithm is studied, and the basic data generated on the electronic music platform are analyzed statistically. Look for the main factors that affect the amount of music auditions by artists, Finally, BP neural network algorithm is used to predict the total amount of music auditions of artists in the next month. 2. While using BP neural network algorithm to predict the amount of music auditions of artists, the quadratic exponential smoothing method is used. The Auto-regressive moving average model is used to predict the audition quantity of entertainers. Finally, the prediction results of three prediction methods are compared. 3. A music trend prediction system based on BP neural network algorithm is designed and implemented. The system is based on J2EE platform. Combined with web development technology, database technology, data mining technology to develop. Can not understand the quadratic exponential smoothing method, Staff members of prediction algorithms such as the autoregressive moving average model and BP neural network algorithm can also use this system to predict the music auditions of artists in the next stage. In order to judge which artists will represent the next stage of music trends. Finally, this paper summarizes the content of the study, and makes a prospect for the next work.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52;TP183

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本文編號：1547618