Paper Info
Title
Searching for Audio by Sketching Mental Images of Sound: A Brave New Idea for Audio Retrieval in Creative Music Production.
Abstract
We propose a new paradigm for searching for sound by allowing users to graphically sketch their mental representation of sound as query. By conducting interviews with professional music producers and creators, we find that existing, text-based indexing and retrieval methods based on file names and tags to search for sound material in large collections (e.g., sample databases) do not reflect their mental concepts, which are often rooted in the visual domain and hence are far from their actual needs, work practices, and intuition. As a consequence, when creating new music on the basis of existing sounds, the process of finding these sounds is cumbersome and breaks their work flow by being forced to resort to browsing the collection. Prior work on organizing sound repositories aiming at bridging this conceptual gap between sound and vision builds upon psychological findings (often alluding to synaesthetic phenomena) or makes use of ad-hoc, technology-driven mappings. These methods foremost aim at visualizing the contents of collections or individual sounds and, again, facilitating browsing therein. For the purpose of indexing and querying, such methods have not been applied yet. We argue that the development of a search system that allows for visual queries to audio collections is desired by users and should inform and drive future research in audio retrieval. To explore this notion, we test the idea of a sketch interface with music producers in a semi-structured interview process by making use of a physical non-functional prototype. Based on the outcomes of this study, we propose a conceptual software prototype for visually querying sound repositories using image manipulation metaphors.
Year
DOI
Venue
2016
10.1145/2911996.2912021
ICMR
Keywords
Field
DocType
audio retrieval, retrieval by sketch, cross-domain retrieval, music production
Image manipulation,Computer science,Search engine indexing,Intuition,Mental image,Software,Artificial intelligence,Work flow,Machine learning,Sketch,Mental representation
Conference
Citations 
PageRank 
References 
2
0.39
21
Authors
2
Name
Order
Citations
PageRank
Peter Knees159451.71
Kristina Andersen2518.89