一种在TwinVQ音频压缩的压缩域中提取音乐单元以短语化音乐数据的方法

Motohiro Nakanishi, M. Kobayakawa, M. Hoshi, Tadashi Ohmori
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引用次数: 6

摘要

将音乐数据分词成有意义的音乐片段(例如,小节和乐句)的方法是分析音乐数据的重要功能。为了实现这一功能,我们提出了一种在TwinVQ音频压缩(MPEG-4音频)压缩域中提取音乐数据单元(音乐单元)的方法。我们的关键思想是从TwinVQ音频压缩编码步骤中计算的自相关系数序列中提取音乐单元。我们把自相关系数的序列称为“自相关序列r”。我们使用第k个自相关序列r/下标k/ (k= 1,2,…, 20),用于提取音乐数据的音乐单元。首先,我们计算第k个自相关序列r/sub k/的j/sub k/-第k自相关系数a/sub k//sup j//sub k/ (j/sub k/= 38,39,…, 208;K = 1,2,…,20)。其次,用于检测序列中的峰值(a/sub k//sup 38/, a/sub k//sup 39/,…(a/sub k//sup 208/),拉普拉斯滤波器应用于序列。然后我们得到p/下标k/阶的最大微分系数。最后,我们使用p/下标k/来计算音乐单元。为了评估该方法提取音乐单元的性能,我们收集了64个音乐数据,并通过对每个数据应用TwinVQ编码器获得自相关序列。然后,我们将提取算法应用于每个自相关序列。实验结果表明,该方法能够很好地提取乐句数据中的音乐单元。
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A method for extracting a musical unit to phrase music data in the compressed domain of TwinVQ audio compression
A method for phrasing music data into meaningful musical pieces (e.g., bar and phrase) is an important function to analyze music data. To realize this function, we propose a method for extracting a unit of music data (musical unit) in the compressed domain of TwinVQ audio compression (MPEG-4 audio). Our key idea is to extract a musical unit from a sequence of autocorrelation coefficients computed in the encoding step of TwinVQ audio compression. We call the sequence of the autocorrelation coefficients the "autocorrelation sequence r". We use the k-th autocorrelation sequence r/sub k/ (k=1, 2, ..., 20) of music data for extracting a musical unit of music data. First, we calculate the j/sub k/-th autocorrelation coefficient a/sub k//sup j//sub k/ of the k-th autocorrelation sequence r/sub k/ (j/sub k/=38, 39, ..., 208; k=1, 2, ...,20). Second, for detecting the peak in the sequence (a/sub k//sup 38/, a/sub k//sup 39/, ..., a/sub k//sup 208/), the Laplacian filter is applied to the sequence. We then obtain the order p/sub k/ for which the maximum differential coefficient is attained. Finally, we compute the musical unit using p/sub k/. To evaluate the performance of extracting the musical unit by our method, we collected 64 music data and obtained autocorrelation sequences by applying the TwinVQ encoder to each data. We then applied our extraction algorithm to each autocorrelation sequence. The experimental results reveal a very good performance in the extraction of a musical unit for phrasing music data.
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