论文标题
调查唱歌语音分离，以进行多音音乐中的唱歌语音检测
Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music
论文作者
论文摘要
识别歌曲中的声音部分的歌声检测（SVD）是音乐信息检索（MIR）的重要任务。这项任务仍然具有挑战性，因为歌声与伴奏音乐有所不同，特别是对于一些复杂的复音音乐，例如合唱音乐录音。为了解决这个问题，我们在丢弃伴奏的干扰时研究了唱歌的语音检测。拟议的SVD有两个步骤：i。歌声分离（SVS）技术首先被用来过滤唱歌声音的潜在部分。 ii。在时间域中发声的连续性后，长期经常性卷积网络（LRCN）用于学习组成特征。此外，为了消除异常值，我们选择使用中间过滤器进行时间域平滑。实验结果表明，所提出的方法在两个公共数据集（Jamendo Copus和RWC POP数据集）上胜过现有最新的最新方法。
Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR). The task remains challenging since singing voice varies and intertwines with the accompaniment music, especially for some complicated polyphonic music such as choral music recordings. To address this problem, we investigate singing voice detection while discarding the interference from the accompaniment. The proposed SVD has two steps: i. The singing voice separation (SVS) technique is first utilized to filter out the singing voice's potential part coarsely. ii. Upon the continuity of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN) is used to learn compositional features. Moreover, to eliminate the outliers, we choose to use a median filter for time-domain smoothing. Experimental results show that the proposed method outperforms the existing state-of-the-art works on two public datasets, the Jamendo Corpus and the RWC pop dataset.
