This course will explore how scholars and practitioners use musical data, both in audio and notated formats. Students will be given the opportunity to develop skills in encoding, analysing, categorizing, and curating music recordings and notated music. These skills will be developed by encouraging an intimate understanding of the nature of different musical formats, an appreciation of their uses, and approaches to computational analyses of their electronic manifestations.

1x1hr lecture and 1x1hr seminar or lab session per week as scheduled in MyCampus. This is one of the Honours Information Studies options and may not run every year. The options that are running this session are available in MyCampus.

ARTMED4038 Music Curation and Analytics

INFOSTUD4014 Music Curation and Analytics

None

Project Output Dataset: Students will have to complete an analytical project based on a digital dataset

Report: The dataset will be accompanied with a report of 2,500 words

Project Output Dataset 60%

Report: 40%.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

This course aims to:

■ deliver an introduction to digital methodologies in music analysis and curation, and the ways music data is manifested and visualized;

■ explain and apply methods for analyzing digital audio data;

■ explore the principles and best practice of notated music encoding, such as using XML;

■ provide training in methods of encoded notated music analysis;

■ apply relevant and appropriate encoding and analytics

By the end of this course, students will be able to:

■ formulate relevant research questions for music data;

■ assess how encoding and computational analyses can help answer research questions about music data;

■ evaluate how digital manifestations can change the ways in which music is used and analysed;

■ judge the effectiveness of computational methodologies in music research;

■ design and apply an appropriate encoding to a musical score, such as XML;

■ identify and implement standards and best practices generally accepted by the research community;

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.