Abstract
In this paper, we describe various application scenarios for archive management, broadcast/stream analysis, media search and media forensics which require the detection and accurate localization of unknown partial audio matches within items and datasets. We explain why they cannot be addressed with state-of-the-art matching approaches based on fingerprinting, and propose a new partial matching algorithm which can satisfy the relevant requirements. We propose two distinct requirement sets and hence two variants / settings for our proposed approach: One focusing on lower time granularity and hence lower computational complexity, to be able to deal with large datasets, and one focusing on fine-grain analysis for small datasets and individual items. Both variants are tested using distinct evaluation sets and methodologies and compared with a popular audio matching algorithm, thereby demonstrating that the proposed algorithm achieves convincing performance for the relevant application scenarios beyond the current state-of-the-art.
Highlights
IntroductionAudio matching is a topic which was thoroughly investigated over the last decades: Being able to match a query file against a reference dataset is important for many application domains, including broadcast monitoring, music identification, copyright management, etc
Audio matching is a topic which was thoroughly investigated over the last decades: Being able to match a query file against a reference dataset is important for many application domains, including broadcast monitoring, music identification, copyright management, etc.Typical requirements for such identification use cases include robustness against distortion, the ability to deal with very large reference datasets, low computational cost and efficient search
The paper is organised as follows: In Section 2 we are going to outline in details the relevant application scenarios for partial audio matching, and their corresponding requirements; Section 3 describes the existing state-of-the-art approaches for classic query-based audio matching; Section 4 presents our approach for partial audio matching, which is evaluated in Section 5 using two distinct requirement sets
Summary
Audio matching is a topic which was thoroughly investigated over the last decades: Being able to match a query file against a reference dataset is important for many application domains, including broadcast monitoring, music identification, copyright management, etc. Typical requirements for such identification use cases include robustness against distortion, the ability to deal with very large reference datasets, low computational cost and efficient search. Several application scenarios have emerged which do not, or do require query-based, robust matching (see Fig. 1)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call