Difference between revisions of "2018:Patterns for Prediction Results"

From MIREX Wiki
m (Training and Test Datasets)
m (Datasets and Algorithms)
Line 13: Line 13:
 
== Datasets and Algorithms ==
 
== Datasets and Algorithms ==
  
...
+
The training datasets were...
  
There were no submissions to the audMono or audPoly variants of the task this year.
+
Submissions to the symMono and symPoly variants of the task are listed in Table 1. There were no submissions to the audMono or audPoly variants of the task this year. The task captains prepared a first-order Markov model (MM) over a state space of measure beat and key-centralized MIDI note number. This enabled evaluation of the implicit subtask, and can also serve as a point of comparison for the explicit task. It should be noted, however, that this model had access to the full song/piece – not just the prime – so it is at an advantage compared to EN1 and FC1 in the explicit task.
  
  
Line 38: Line 38:
 
|-
 
|-
 
         ! MM
 
         ! MM
| Markov model  ||  style="text-align: center;" | N/A || Intended as 'baseline'
+
| Markov model  ||  style="text-align: center;" | N/A || For purposes of comparison
 
|-
 
|-
 
         |- style="background: green;"
 
         |- style="background: green;"
Line 50: Line 50:
 
|-
 
|-
 
         ! MM
 
         ! MM
| Markov model  ||  style="text-align: center;" | N/A || Intended as 'baseline'
+
| Markov model  ||  style="text-align: center;" | N/A || For purposes of comparison
 
|-
 
|-
 
|}
 
|}
  
 
'''Table 1.''' Algorithms submitted to Patterns for Prediction 2018.
 
'''Table 1.''' Algorithms submitted to Patterns for Prediction 2018.
 +
 +
'''(DESCRIBE SUBMISSIONS EN1 AND FC1 BRIEFLY HERE.)'''
  
 
== Results ==
 
== Results ==

Revision as of 09:27, 18 September 2018

Introduction

THIS PAGE IS UNDER CONSTRUCTION!

The task: ...

Contribution

...

For a more detailed introduction to the task, please see 2018:Patterns for Prediction.

Datasets and Algorithms

The training datasets were...

Submissions to the symMono and symPoly variants of the task are listed in Table 1. There were no submissions to the audMono or audPoly variants of the task this year. The task captains prepared a first-order Markov model (MM) over a state space of measure beat and key-centralized MIDI note number. This enabled evaluation of the implicit subtask, and can also serve as a point of comparison for the explicit task. It should be noted, however, that this model had access to the full song/piece – not just the prime – so it is at an advantage compared to EN1 and FC1 in the explicit task.


Sub code Submission name Abstract Contributors
Task Version symMono
EN1 Algo name here PDF Eric Nichols
FC1 Algo name here PDF Florian Colombo
MM Markov model N/A For purposes of comparison
Task Version symPoly
FC1 Algo name here PDF Florian Colombo
MM Markov model N/A For purposes of comparison

Table 1. Algorithms submitted to Patterns for Prediction 2018.

(DESCRIBE SUBMISSIONS EN1 AND FC1 BRIEFLY HERE.)

Results

An intro spiel here...

(For mathematical definitions of the various metrics, please see 2018:Patterns_for_Prediction#Evaluation_Procedure.)

SymMono

Here are some results (cf. Figures 1-3), and some interpretation. Don't forget these as well (Figures 4-6), showing something.

Remarks on runtime appropriate here too.

SymPoly

And so on.

Discussion

...

Berit Janssen, Iris Ren, Tom Collins.

Figures

SymMono

2017 Mono R est.png

Figure 1. Establishment recall averaged over each piece/movement. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?

2017 Mono P est.png

Figure 2. Establishment precision averaged over each piece/movement. Establishment precision answers the following question. On average, how similar is the most similar ground-truth pattern prototype to an algorithm-output pattern?


SymPoly

2017 Poly R est.png

Figure 12. Establishment recall averaged over each piece/movement. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?

2017 Poly P est.png

Figure 13. Establishment precision averaged over each piece/movement. Establishment precision answers the following question. On average, how similar is the most similar ground-truth pattern prototype to an algorithm-output pattern?

Tables

SymMono

Click to download SymMono pattern retrieval results table

Click to download SymMono compression results table

SymPoly

Click to download SymPoly pattern retrieval results table

Click to download SymPoly compression results table