  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.
