Player vs Transcriber: A Game approach to automatic music transcription

Southall, Carl and Stables, Ryan and Hockman, Jason (2018) Player vs Transcriber: A Game approach to automatic music transcription. In: International Society of Music Information Retrieval Conference, 23rd-27th September, 2018, Paris, France.

[img]
Preview
Text
player-transcriber-game.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB)

Abstract

State-of-the-art automatic drum transcription (ADT) ap-proaches utilise deep learning methods reliant on time-consuming manual annotations and require congruence be-tween training and testing data. When these conditionsare not held, they often fail to generalise. We proposea game approach to ADT, termed player vs transcriber(PvT), in which a player model aims to reduce transcrip-tion accuracy of a transcriber model by manipulating train-ing data in two ways. First, existing data may be aug-mented, allowing the transcriber to be trained using record-ings with modified timbres. Second, additional individualrecordings from sample libraries are included to generaterare combinations. We present three versions of the PvTmodel:AugExist, which augments pre-existing record-ings;AugAddExist, which adds additional samples ofdrum hits to theAugExistsystem; andGenerate, whichgenerates training examples exclusively from individualdrum hits from sample libraries. The three versions areevaluated alongside a state-of-the-art deep learning ADTsystem using two evaluation strategies. The results demon-strate that including the player network improves the ADTperformance and suggests that this is due to improved gen-eralisability. The results also indicate that although theGeneratemodel achieves relatively low results, it is a vi-able choice when annotations are not accessible.

Item Type: Conference or Workshop Item (Paper)
Dates:
DateEvent
25 May 2018Accepted
27 September 2018Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-04 - software engineering
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH25 - design, and creative and performing arts > CAH25-02 - performing arts > CAH25-02-02 - music
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Jason Hockman
Date Deposited: 13 Aug 2018 08:22
Last Modified: 03 Mar 2022 15:33
URI: https://www.open-access.bcu.ac.uk/id/eprint/6182

Actions (login required)

View Item View Item

Research

In this section...