1. A review of audio fingerprinting (Cano et al. 2005)
mainly based on
A review of audio fingerprinting (Cano et al. 2005)
My name is Denis Lebel and I will talk about Interactive Rendering of Suggestive Contours with Temporal Coherence.
presented by
Denis Lebel

2. Presentation Outline Introduction Desired Properties Usage Modes
Applications
Fingerprinting Framework
Front-end
Fingerprint Models
Similarity Measures and Searching Methods
Hypothesis Testing
Conclusion
References
To start off this presentation, we will look at an example of suggestive contours vs true contours and clarify the terminology I will be using throughout my presentation. I will then give a brief overview of suggestive contours, so you get a better idea of what they actually are. Then, we’ll move the motivation behind the work of this paper and cover the various contributions by the author. I will end this presentation by giving you future challenges for suggestive contours. If time permits it, you will also have a chance to watch a live demonstration of suggestive contours… One more thing: feel free to ask questions if you don’t understand and I’ll do my best to answer or will refer you to a more adequate source of information.
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

3. Introduction Idea Audio Fingerprint Fingerprinting System
An attempt to mimic human music recognition abilities
Audio Fingerprint
Unique identifier of an audio signal
Content-based signature that summarizes an audio recording
Uses relevant (perceptual) acoustics characteristics of signal
Fingerprinting System
Database of known fingerprints
Query system
Unidentified
song
Match
Return
Information
Search
Collection
Of all known
songs NO
Match
Keep
Looking…
Figure 1: General idea of a fingerprinting system
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

4. Desired Properties Accuracy Reliability Robustness Granularity
Function of correct, missed, and wrong identifications
Reliability
Correct identification method
Robustness
Ability to accurately identify an item (no matter how compressed or distorted it is)
Granularity
Ability to identify a signal from a short excerpt
Security
Vulnerability to cracking
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

5. Desired Properties Versatility Scalability Complexity Fragility
Ability to identify a signal regardless of audio format
Scalability
Performance with very large databases
Complexity
Computational costs of fingerprint extraction, size of fingerprint, search complexity, comparison complexity, etc.
Fragility
Integrity verification (detection of changes in content)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

6. Desired Properties
Properties are interrelated and dependent of system purpose
Generally speaking, fingerprint should be:
A perceptual digest of the recording
Invariant to distortions
Compact
Easily computable
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

7. Usage Modes Identification Integrity Verification
Content identification of an audio signal
Integrity Verification
Detection of data alteration
Figure 2: Content-based audio identification
framework. (Cano et al. 2005)
Figure 3: Integrity verification framework.
(Cano et al. 2005)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

8. Usage Modes Watermarking support
Audio fingerprints can be used to derive secrets keys from the audio content
Content-based Audio Retrieval and Processing
Extraction of audio features (i.e., low-level and high-level descriptors)
Fingerprints can be used to retrieve similar content (i.e., query-by-example scheme)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

9. Applications Audio Content Monitoring and Tracking
At the distributor end
At the transmission channel
At the consumer end
Added-Value Services
Content information describing audio excerpt (e.g., tempo)
Meta-data describing musical work (e.g., composer, year, …)
Other information (e.g., album cover)
Integrity Verification Systems
Audio fingerprints can be used to ensure user’s audio files have the best quality available
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

10. Presentation Outline Introduction Desired Properties Usage Modes
Applications
Fingerprinting Framework
Front-end
Fingerprint Models
Similarity Measures and Searching Methods
Hypothesis Testing
Conclusion
References
To start off this presentation, we will look at an example of suggestive contours vs true contours and clarify the terminology I will be using throughout my presentation. I will then give a brief overview of suggestive contours, so you get a better idea of what they actually are. Then, we’ll move the motivation behind the work of this paper and cover the various contributions by the author. I will end this presentation by giving you future challenges for suggestive contours. If time permits it, you will also have a chance to watch a live demonstration of suggestive contours… One more thing: feel free to ask questions if you don’t understand and I’ll do my best to answer or will refer you to a more adequate source of information.
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

11. Fingerprinting Framework
Figure 4: Content-based audio identification framework. (Cano et al. 2005)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

12. Fingerprinting Framework
Fingerprint Extraction: Front-End
Figure 5: Fingerprint Extraction Framework. (Cano et al. 2005)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

13. Fingerprinting Framework
Fingerprint Extraction: Fingerprint Modeling
Idea: Reduce redundancies
Reduce size of fingerprint
Similarity measure and search method depends on the model chosen
Several techniques can be used (for a summary: Cano et al. 2005)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

14. Fingerprinting Framework
Figure 4: Content-based audio identification framework. (Cano et al. 2005)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

15. Fingerprinting Framework
Fingerprint Extraction: Similarity Measures
Related to type of model chosen
Correlation metric is common
Example: Euclidean distance
Figure 6: a) Fingerprint block of original clip
b) fingerprint block of a compressed version.
c) Difference (error) (Haitsma et al. 2002)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

16. Fingerprinting Framework
Fingerprint Extraction: Searching Methods
Using brute-force search is inappropriate for large database
Idea: Optimizing the search
Some possible optimizations
Pre-computing distances offline
Filtering unlikely candidates with a cheap similarity measure
Candidate pruning
Others…
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

17. Fingerprinting Framework
Fingerprint Extraction: Hypothesis Testing
Idea: Whether the query is present in the repository
A threshold must be used and it depends on:
Fingerprint model
Similarity of fingerprints in the database
Database size
Discriminative information of the query
The larger the database, the higher the probability of wrong match
False Acceptance Rate (FAR)
False Rejected Rate (FRR)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

18. Conclusion
Most existing systems fall more or less into this generic framework
Large databases still represent a challenge (scalability, complexity, accuracy…)
P2P systems might be the future (e.g., Music2Share)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

19. References
Cano, P., E. Batlle, T. Kalker, and J. Haitsma A review of audio fingerprinting. The Journal of VLSI Signal Processing 41: 271–84.
Haitsma, J., and T. Kalker A highly robust audio fingerprinting system. Proceedings of the International Symposium on Music Information Retrieval. 107–15.
Kalker, T., D. Epema, P. Hartel, R. Langendijk, and M. Van Steen Music2Share: Copyright-compliant music sharing in P2P systems. Proceedings of the IEEE 92 (6): 961–70.
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

20. Links http://www.shazam.com/ http://www.relatable.com/
MUMT-611: Music Information Acquisition, Preservation, and Retrieval