Teosto's researcher Turo Pekari presented the idea of Music Recognition Pilots @ Finlandia-talo.

1. What’s this song?
Music Recognition Pilots 2012-2013
Technology, Music Rights, Licensing
21.3.2013
Turo Pekari

2. Is it possible to
create setlists from
live gigs and clubs
using automated
music recognition
system?
2

3. Music Recognition Pilots 2012-2013
• Live Music •DJ/Club Music
Recognition monitoring
• 15-17.6.2013 • 12/2012-
• Provinssirock 03/2013
Finland
3

4. 4

5. Partners
Nightwish
PMMP
Notkea Rotta
Darude
Orkidea
K-System
Riku Kokkonen
5
Kuvat: Yleisradio, OKM, Provinssirock

6. Live Music Recognition Pilot
Provinssirock 15-17.6.2012
• 3 gigs, 3 days
• (Friday) Nightwish
• (Saturday) PMMP
• (Sunday) Notkea Rotta
•Recordings: Mixing desk/audience (wav/mp3)
•Reference Data: artist discographies
Kuva: Turo Pekari

7. Live music recognition is tricky
• Identification of songs is
more complex than in mere
matching of 2 identical
audio files
• No commercial solutions
available
Kuva: Turo Pekari

8. Why?
1. It’s never been done before!
2. …but it’s possible.
3. Untapped potential?
-More efficient reporting, new services
Kuva: Turo Pekari

9. PMMP
NIGHTWISH
NOTKEA
9 ROTTA

10. • System works already with some restrictions (genre)
• Identification does not need high quality audio to succeed.
• No significant differences between results from mixing desk and
audience recordings.
• Restrictions: limited size of reference data
Kuva: Turo Pekari

11. DJ/Club Music Monitoring
• 12/2012-03/2013 • BMAT Vericast Audio
Identification technology
• 4 Finnish DJs
• Darude, K-System, • How does broadcast
Orkidea, Riku Kokkonen monitoring technology
handle DJ/club music
• 5 DJ sets identification?
• 4 line out signal
• 1 live DJ-set
11
Kuva: Turo Pekari

12. Reference Audio Sets
• ORIGINAL • VERSION • BIG DATA
• 55 songs •166 songs • 10K of popular
• 76% coverage • Other versions club music
• Original reference (e.g. original, including the
collection matched extended version, ORIGINAL
against recordings club, mix, radio references
edit) • Performance
• Used to bias the estimations and
results and validation
investigate the
relation between
the different
versions.
12

13. • Good results from identification straight from mixing desk line-out
• Best results from K-System DJ-set
• Mixing and processing live signal and line-out signal in second
Darude DJ-set caused difficulties
13 Kuva: Turo Pekari

14. 3 scenarios for implementing the
system (live music and club music)
1. Big festivals 2. Integrated 3. Tour/Artist-
systems for active specific service
-Best efficiency live venues
-An alternative for
-Royalties - Clubs, cruise ships manual reporting
distributed vs. cost
of service - Both live and DJ
music recognition
- New services for offered as one
the audience based service
on data
Kuva: Turo Pekari

15. For more information:
• Turo Pekari (turo.pekari@teosto.fi)
• Ano Sirppiniemi (ano.sirppiniemi@teosto.fi)
15