Exploratory Analysis of a Large Flamenco Corpus using an Ensemble of Convolutional Neural Networks as a Structural Annotation Backend
This work addresses the need for automated musicological analysis in flamenco, offering tools to enhance metadata and bridge the semantic gap between audio and high-level concepts, though it is incremental as it applies existing methods to a new domain.
The researchers tackled the problem of analyzing a large corpus of flamenco music by developing computational tools using an ensemble of Convolutional Neural Networks to automatically annotate structural elements like vocals, guitar, and hand-clapping, enabling visualization, statistical extraction, and detection of a cappella and instrumental recordings.
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.