Author(s): Preeth Raguraman, Mohan R, Midhula Vijayan
Published: March 2019 in 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
URL to article
Research Focus Area: Grading systems that work and are meaningful in the 21st century especially for colleges and employeers, and for topics like PE, arts, and health
Abstract:
Music is possibly the most impactful bonding over the society and culture. The process of perusing the music in classrooms is considered as a costly affair for the humans in rural areas. Although the tutorials (i.e., video lectures) are usually available for free access in internet, the process of learning and evaluation yet depends on conventional teacher-student affair. So, the need for an automated tool designating the process of analysis cum assessment of music is formulated in the music eternity. This proposed model focuses on providing a solution for this use case exactly where the users can play notes or music pieces on a musical instrument (i.e., piano ) and followed by evaluating their performance against a chosen benchmark audio file, named as ‘teacher’ file. The technique considers various features such as loudness, tempo, rolloff frequency, kurtosis, skewness and centroid associated with piano for evaluation process. The model emphasizes towards to distinguish the characteristics of different keys and it was achieved with the help of Essentia onset function and LibROSA python package. The evaluation was carried through a set of stages such as normalization of features, pattern matching to extract an effective grading sheet of the played tune (i.e., ‘Student’ file). The drawbacks such as identification of the sequence of musical tones and noise onsets were avoided using a pattern matching framework, named as REMOVE_NOISY_ONSETS. The performance factor is fixed based on the notes that were missed by the user and the extra notes played by the user. Hence, this technique is of cutting-edge technology in the expertise of music education using technology trends. The proposed tool also helps people, those were deprived of learning the instrument of their choice by giving them an easy-to-use software tool to evaluate themselves and also give a lucid user interface to review their performance.
Research Question(s):
How does the libROSA based assessment tool make digital music learning technology accessible to teachers and students in rural settings?
Methods:
product testing
Setting:
rural india
Key Findings:
- The use of Music Information Retrieval (MIR technologies) and assessment technologies in music learning applications is a budding multidisciplinary field of research. - MIR technology could allow a student to assess their own playing against a pre-recorded teacher’s version of the same piece - The researchers predict there will be immense developments in the field of music education and learning with computer-based technologies in the future.
Implications:
- There are still many obstacles to the integration of MIR technologies in music learning programs for rural communities. This points to the need for collaboration between music technology engineers and community members.