  We present computational tools that we developed for the analysis of a large
corpus of flamenco music recordings, along with the related exploratory
findings. The proposed computational backend is based on a set of Convolutional
Neural Networks that provide the structural annotation of each music recording
with respect to the presence of vocals, guitar and hand-clapping ("palmas").
The resulting, automatically extracted annotations, allowed for the
visualization of music recordings in structurally meaningful ways, the
extraction of global statistics related to the instrumentation of flamenco
music, the detection of a cappella and instrumental recordings for which no
such information existed, the investigation of differences in structure and
instrumentation across styles and the study of tonality across instrumentation
and styles. The reported findings show that it is feasible to perform a large
scale analysis of flamenco music with state-of-the-art classification
technology and produce automatically extracted descriptors that are both
musicologically valid and useful, in the sense that they can enhance
conventional metadata schemes and assist bridging the semantic gap between
audio recordings and high-level musicological concepts.
