Presentation slide deck by Anggarda Prameswari for her Master Project Information Sciences "ELEVATOR ANNOTATOR Local Crowdsourcing on Audio Annotation for The Netherlands Institute for Sound and Vision" http://www.victordeboer.com/wp-content/uploads/2017/07/2589263_ElevatorAnnotator_FinalVersion.pdf Crowdsourcing and other human computation techniques have proven their use for collecting large numbers of annotations, including in the domain of cultural heritage. Most of the time, crowdsourcing campaigns are done through online tools. Local crowdsourcing is a variant where annotation activities are based on specific locations related to the task. This study investigates a local crowdsourcing application for audio annotations. We describe a specific use for annotating archival audio content to enrich its metadata. We developed a platform called “Elevator Annotator”, to be used on-site. The platform is designed as a standalone Raspberry Pi-powered box which can be placed in an on-site elevator for example. It features a speech recognition software and a button-based UI to communicate with participants. We evaluate the effectiveness of the platform in two different locations and modes of interaction through a local crowdsourcing experiment. The results show that the local crowdsourcing approach is able to achieve annotations with 61% of accuracy, up to 4 annotations per hour. Given that these results were acquired from one elevator, this practice can be a promising method of eliciting annotations from on-site participants.

1. Elevator Annotator
Local Crowdsourcing on Audio Annotation for
Netherlands Institute for Sound and Vision
Anggarda Prameswari 2589263@student.vu.nl
Supervisor: Victor de Boer
Daily Supervisor: Themistoklis Karavellas

2. Elevator Annotator
What is it about?

3. ▷ The rise of crowdsourcing practice in the community
→ Crowdsourcing is seen to be an attractive solution to acquire
annotations cheaply and quickly
▷ Local crowdsourcing:
→ Participants do the given tasks on site while overcoming
challenges and influences posed by physical environment
→ Example: Local visitors in a museum help annotate the painting
collections
What about audio collections?
Background

4. Audio file
▷ An audio file consists of multiple information (metadata)
▷ There are many musical instruments involved in a song
▷ In a fragment of seconds there will different instrument
playing dominantly

5. Case study
▷ Netherlands Institute for Sound and Vision
→ Largest audiovisual archive in the Netherlands
→ To collect and preserve audiovisual heritage
→ Make it available to as many users as possible
▷ How?
→ Enriching the audio collection’s content by having
additional information of the instruments
→ Make use of local crowdsourcing with pervasive
computing
→ Experimenting in an elevator (Why?)
→ Used frequently, people will mostly be idle, thus
ideal to ask for annotation

6. What do you hear?

7. Research Questions
“Can local crowdsourcing be used to enrich the audio collection
content with improved quality?”
▷ What are the requirements for the crowdsourced annotations
according to its technical and ethical aspects?
▷ How do different physical locations of the local crowdsourcing
affect the result?
▷ How do different type of user interactions of the local
crowdsourcing affect the result?

8. Approach
How will it be carried out?

9. Approach
(February - March)
▷ Implementation → Develop a system using Raspberry Pi as the platform

10. Approach
(April - May)
▷ Experiments
→ Locations: Offers
different institutional
properties
→ User interfaces:
Offers different
feedback to the user

11. ▷ Evaluation
→ Accuracy of musical instrument identification
→ Willingness of crowd to participate
→ Participation statistic
▷ Result
→ Source codes with the prototype
→ Comprehensive report of evaluation
Approach
(continuously - June)

12. Thanks!
Any questions?