1. Voice Separation: A 15-minute Introduction
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 Voice Separation Techniques
Voice Separation Systems
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 Purpose Idea 2 different contexts
Transcription of low-level musical data into score notation
Theme finding and music analysis
Idea
Separation of notes into voices with possible chords (in a polyphonic context)
2 different contexts
Explicit Polyphony
Multiple notes sounding at one instant
Assuming no single voice can produce 2 notes simultaneously
Implicit Polyphony
At most one note sounding at any instant
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

4. Techniques Split Point Separation Idea:
Split pitch range into disjoint intervals
Problem:
Works only on non-overlapping voices
Notes:
One of the simplest methods
Used in most commercial systems
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

5. Techniques Rule-Based Approach Idea:
Take advantage of the voice-leading rules used by composer
Rules examples:
Polyphonic motion
Succeeding notes intervals
Problems:
Many such rules, specific to composer
Errors occur with major overlapping of voices
Note:
Better approach than split point
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

6. Techniques Local Optimization Approach Idea:
Using a heuristic algorithm
Iterative process that finds the best solution from a given set at each step
Problem:
Not meant to find the correct voice separation but rather to provide reasonable solutions in different contexts
Note:
Complex approach but seems to give better results
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

7. Techniques Others Contig Mapping Approach
Same-Voice Predicate (learned decision tree) along with voice-numbering algorithm …
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

8. Systems VoiSe – University of Massachusetts Amherst (Kirlin 2005)
VoSA – University of Southern California (Chew 2004)
Melisma Music Analyzer – Carnegie Mellon University (Sleator and Temperley 2001)
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

9. Systems VoSA Interface Screenshot – from VoSA website
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

10. Conclusion
Despite the different existing techniques, there is still no perfect solution to voice separation.
MUMT-611: Music Information Acquisition, Preservation, and Retrieval

11. References
Kirlin, P., and P. Utgoff VoiSe: Learning to segregate voices in explicit and implicit polyphony. Proceedings of the International Conference on Music Information Retrieval. 552–7.
Chew, E., and X. Wu Seperating voices in polyphonic music: A contig mapping approach. Proceedings of the International Symposium on Computer Music Modeling and Retrieval. 1–20.
Kilian, J., and H. Hoos Voice separation: A local optimisation approach. Proceedings of the International Conference on Music Information Retrieval. 39–46.
MUMT-611: Music Information Acquisition, Preservation, and Retrieval