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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/69273

    Title: 基於深度學習之AAC壓縮域翻唱歌快速檢索;Fast Cover Song Retrieval in AAC Domain based on Deep Learning
    Authors: 張育瑞;Chang,Yu-ruey
    Contributors: 通訊工程學系
    Keywords: 音樂檢索;翻唱歌曲;AAC;深度學習;music information retrieval;cover song;AAC;deep learning
    Date: 2015-11-16
    Issue Date: 2016-01-05 18:07:05 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著多媒體資料的增加,如何從龐大的資料庫中快速找到使用著有興趣的資料成為愈來愈重要的議題。傳統資料檢索的方法大多使用關鍵字來做搜尋,但需要大量人力來為資料先做標記,隨著資料量的增加,關鍵字標記的方法變得較不具彈性。內涵式檢索方法是較自然的方式,也可以避免不同人對同一首歌給定標記不一樣的問題。
    ;With the increasing of multimedia data, it becomes more and more important to quickly search the interests from large databases. Keyword annotation is the traditional approach, but it needs large amount of manual effort to annotate the keyword. As the size of data increases, the keyword annotation approach becomes infeasible. Content-based retrieval is more natural, it extracts features from music content to create a representation that overcomes human labeling errors.
    This thesis focuses on the AAC file which is widely used by streaming internet sources. Here, the proposed system directly maps the modified discrete cosine transform coefficients (MDCT) into a 12-dimensional chroma feature. We combine frames to a segment as the input of deep learning, deep learning can automatically find more meaningful features of music data. We also applied sparse autoencoder to reduce dimensionality of songs. With these efforts, significant matching time can be saved. The experimental results show that the proposed method can reach 0.505 of mean reciprocal rank (MRR) and save over 70% matching time compared with conventional approaches.
    Appears in Collections:[通訊工程研究所] 博碩士論文

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