Panako: a scalable acoustic fingerprinting system handling time-scale and pitch modification

In this paper a scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented. The aim of acoustic fingerprinting is to identify identical, or recognize similar, audio fragments in a large set using condensed representations of audio signals, i.e. fingerprints. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The new system, presented here, handles a variety of distortions well. It is designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable. After a query, the system returns the start time in the reference audio, and the amount of pitch shift and tempo change that has been applied. The design of the system that offers this unique combination of features is the main contribution of this research. The fingerprint itself consists of a combination of key points in a Constant-Q spectrogram. The system is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30.000 songs. The results show that the system responds quickly and reliably on queries, while handling time and pitch scale modifications of up to ten percent

OLAF : Overly Lightweight Acoustic Fingerprinting

Olaf is a portable, landmark-based, acoustic fingerprint-ing system released as open source software. Olaf runs on embedded platforms, traditional computers and in the browser. Olaf is able to extract fingerprints from an audio stream, and either store those fingerprints in a database, or find a match between extracted fingerprints and stored fingerprints. It implements an algorithm similar to the one described in a classic ISMIR paper and has similar retrieval performance. It facilitates the many use cases acoustic fingerprinting has to offer such as duplicate detection, meta-data coupling, and synchronization

A case for reproduciblity in MIR : replication of 'a highly robust audio fingerprinting system'

This article makes a case for reproducibility in MIR research. Claims made in many MIR publications are hard to verify due to the fact that (i) often only a textual description is made available and code remains unpublished - leaving many implementation issues uncovered; (ii) copyrights on music limit the sharing datasets; and (iii) incentives to put effort into reproducible research -- publishing and documenting code and specifics on data -- is lacking. In this article the problems around reproducibility are illustrated by replicating a MIR work. The system and evaluation described in 'A Highly Robust Audio Fingerprinting System' is replicated as closely as possible. The replication is done with several goals in mind: to describe difficulties in replicating the work and subsequently reflect on guidelines around reproducible research. Added contributions are the verification of the reported work, a publicly available implementation and an evaluation method that is reproducible