Activity Awareness
Feedback loop between the user and their music.
Use the users physiological state to influence the music selection.
The music influences the users mood and mindset.
Potential XPod Platform
Nokia 5500 Sport Phone
Embedded Accelerometers
MP3 Player
http://europe.nokia.com/A4213113
Music and Human Activity
Music has a strong connection with human activity. People like to play music that is appropriate for their
current activity. Music will can affect a persons mental state and
physical activity.
Music Selection
Choose the song that the user would most likely like in this state.
Use the user prior behavior to learn their preferences.
Combine a star rating system with body information.
Song Selection
Choose a Song Randomly
Learning AlgorithmEstimate Preference
Flip coin weighted by the Estimated preference
Play Song
Tails
Heads
Results
0.15
0.25
0.35
0.45
J48 AdaBoost SMO IBk Neural Net
AlgorithmWithout State With State
25%
35%
45%
55%
J48 AdaBoost SMO IBk Neural Net
Algorithm
Perc
ent C
orre
ct
Without State With State
Results
XPod creates a custom context aware playlist. The addition of state information can improve
the accuracy of the learning system.– Prefrence(Song|Time,Activity) ≠ Preference(Song)
Neural Network seems to be best.– Generalizes best– Over trains very quickly
SMO gets the exact.– Does not generalize well.
Network Structure
289 Input neurons 1 Output neuron 1-50 Hidden neurons
– 4 is the best
25%
30%
35%
40%
45%
50%
1 2 3 4 5 10 15 20 25 30 35 40 45 50
Neurons in Hidden Layer
% A
ccura
tely
Cla
ssifie
d
Without State With State
0.17
0.175
0.18
0.185
0.19
0.195
0.2
1 2 3 4 5 10 15 20 25 30 35 40 45 50
Neurons in Hidden Layer
RM
SE
Without State With State
Future Work
Develop a prototype device.– Nokia 5500 Sport– iPaq and Backpaq
Incorporate song metadata– Human Generated, eg Last.fm– Machine Generated
Incorporate other meta information– Location Information– Recent Phone Calls– Weather
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