Clave-direction analysis: A new arena for educational and creative applications of music technology

Abstract

Music Information Research abounds with work on the recognition of style, genre, composer and singer, and the detection of beat, tempo, metre and even emotion. However, some musical attributes remain unexplored. One of these, clave direction, is a rhythmic principle found throughout Latin American music. The purpose of this article is to introduce the significance and potential pedagogical role of automated clave-direction identification, to propose a system-level implementation and to suggest application areas that may benefit performers, educators, students and industry. Clave direction is an inherent feature of rhythmic patterns, not just of the standard sequences commonly associated with clave. Technological aids to composition, arranging, education, search and music production are needed for a growing population of musicians. Existing and developing methods of music technology can be used to fulfil this need. Target applications include rhythmtraining equipment, recording and sequencing software, auto-accompaniment and automated querying of databases or the Internet. Mehmet Vurkaç 28

Application of Computer-Based Auto Accompaniment in Music Education

“…In music teaching and learning, accompaniment is the soul of music, while the computer technology can assist in the completion of music audio, video, and generation of sheet music, etc. [12]. In addition, it also provides recording, editing and processing of sounds, that is, audio extraction, sound editing, and standardization processing of sounds [13].…”

Adversarial Label Learning

“…Clave Direction The task for the Firm Teacher Clave Direction dataset is to classify the clave direction from rhythmic patterns (Vurkaç 2011). The original dataset contains four classes, so we select the two most common classes, resulting in a total of 8,606 examples.…”

Pre-emphasizing Binarized Ensembles to Improve Classification Performance

“…We will limit our presentation of results to a few appropriately selected multiclass databases that are frequently used as benchmark sets for this kind of experiments: the synthetic dataset Firm-Teacher Clave-Direction Classification [17] and two real problems, Satimage [18] and Vehicle [19]. All of them are obtained from the UCI Repository of Machine Learning Databases [20].…”