基于卷积声学基元嵌入的歌手识别
Singer Identification Using Convolutional Acoustic Motif Embeddings
摘要：
弗拉门戈歌唱的特点是音高不稳定，微音调的变化，大的颤音范围，以及高度的旋律变化。这些音乐特征使得弗拉门戈歌手的自动识别成为一项困难的计算任务。在这篇文章中，我们提出了一个端到端的管道弗拉门戈歌手识别的基础上声学主题嵌入。在所采取的方法中，直接从原始音频信号获得的基频被近似。这种近似降低了音频信号的高度可变性，并允许使用顺序模式挖掘技术发现小的旋律模式，从而创建主题字典。几个声学特征，然后使用卷积架构来提取固定长度嵌入的可变长度图案。我们在弗拉门戈歌手识别任务中测试了嵌入的质量，将我们的方法与以前的深度学习架构进行了比较，并研究了识别任务中动机模式和声学特征的影响。结果表明，动机模式识别弗拉门戈歌手发挥了至关重要的作用，通过最大限度地减少要学习的信号的大小，丢弃在识别任务中不相关的信息。提出的深度学习架构优于大规模音频分类问题中使用的密集模型。
英文摘要：
Flamenco singing is characterized by pitch instability, micro-tonal ornamentations, large vibrato ranges, and a high degree of melodic variability. These musical features make the automatic identification of flamenco singers a difficult computational task. In this article we present an end-to-end pipeline for flamenco singer identification based on acoustic motif embeddings. In the approach taken, the fundamental frequency obtained directly from the raw audio signal is approximated. This approximation reduces the high variability of the audio signal and allows for small melodic patterns to be discovered using a sequential pattern mining technique, thus creating a dictionary of motifs. Several acoustic features are then used to extract fixed length embeddings of variable length motifs by using convolutional architectures. We test the quality of the embeddings in a flamenco singer identification task, comparing our approach with previous deep learning architectures, and study the effect of motivic patterns and acoustic features in the identification task. Results indicate that motivic patterns play a crucial role in identifying flamenco singers by minimizing the size of the signal to be learned, discarding information that is not relevant in the identification task. The deep learning architecture presented outperforms denser models used in large-scale audio classification problems.
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