Improving Query-by-Singing/Humming by Combining Melody and Lyric Information
Improving Query-by-Singing/Humming by Combining Melody and Lyric Information
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DOI：
10.1109/taslp.2015.2409735
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
Chung-Che Wang;J. Jang
中科院分区：
文献类型：
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作者：
Chung-Che Wang;J. Jang
提出了一种同时利用旋律信息和歌词信息来改进歌唱/哼唱系统的新方法。首先，通过考虑声学模型之间的相似性，对歌唱和哼唱查询进行区分。对于哼唱查询，应用在MIREX(音乐信息检索评估交换)按歌唱/哼唱任务查询提交中排名第一的仅基音旋律识别方法。对于歌唱查询，使用语音识别技术计算歌词相似度；随后将计算的相似度与旋律距离组合以利用歌词中的附加信息。研究了旋律距离和歌词相似度的几种组合方法。在优化的实验设置下，该系统对前10位的检索结果的错误率降低了51.19%，表明了该方法的可行性。
This paper proposes a novel method for improving query-by-singing/humming systems by using both melody and lyric information. First, singing/humming discrimination is performed to distinguish between singing and humming queries, which is achieved by considering the similarity between acoustic models. For the humming queries, a pitch-only melody recognition method that was ranked first among the MIREX (Music Information Retrieval Evaluation eXchange) query-by-singing/humming task submissions is applied. For the singing queries, a lyric similarity is computed using speech recognition techniques; the computed similarity is subsequently combined with the melody distance to exploit additional information in the lyrics. Several methods for combining melody distance and lyric similarity are investigated. Under the optimal experimental settings, the proposed query-by-singing/humming system achieves 51.19% error rate reduction for the top-10 retrieved results, indicating the feasibility of the proposed method.