1. Recognition of Isolated Instrument Tones by Conservatory Students Asha Srinivasan, David Sullivan, and Ichiro Fujinaga Peabody Conservatory of Music Johns Hopkins University

2. Overview Background Aims Method Set-up of previous experiments Results Conclusions

3. Background Musicians have a remarkable ability to recognize instruments by timbre However, previous experiments using isolated tones suggest that recognition rates range between 36.5% and 90.0%. Recently, timbre-recognition computer models have been able to match or exceed these rates.

4. Aims Verify previous experiments Measure the effect of ensemble experience Generate more detailed baseline data to help evaluate computer performance

5. Method Eighty-eight subjects participated in the experiment. They were undergraduate ear- training students (66), composition students (19), and faculty (3). Personal information was collected: –gender, degree/year, major, primary instrument, # of years formal training, orchestral/band experience, compositional/conducting experience, perfect pitch, # of years ear-training All tones were taken from the McGill University Master Samples.

6. The Tests Two tests were performed: –The first test included four sections, involving 2, 3, 9, and 27 instruments. –In the second test, short training sessions preceded each section, involving 2, 9, and 27 instruments.

7. Training sessions Ex: announce “oboe,” play 2 - 3 oboe samples; announce “sax,” play 2-3 sax samples The 27-instrument sessions were grouped by family and by similar sound

8. List of Instruments

10. Previous experiments and Peabody

11. Recognition rates for previous human experiments

12. Overview of Results Comparison of previous experiments and Peabody Family groupings Comparison of different groups of Peabody subjects Piano, Guitar, Voice (PGV) students vs. Non- PGV students Effect of the short-term training sessions

13. Recognition rates for previous human experiments and Peabody results

14. Previous computer experiments

15. Recognition rates for previous computer and human experiments and Peabody

16. Confusion matrix (2-instr. & 3-instr.)

17. Confusion matrix (9-instr.)

18. Confusion matrix (3D-View)

19. Confusion matrix comparison

20. Confusion matrix (27-instr.)

21. Confusion matrix (3D-View)

22. Confusion matrix (Martin)

23. Confusion matrix (Family grouping for 9-instr. & 27-instr.)

24. Confusion matrix comparison

25. Family vs. Exact Answers

26. Recognition rates for ear-training students, composition students, and faculty

27. Piano, Guitar, Voice (PGV) students vs. Non-PGV students

28. Effects of training on ear-training (47) and composition (6) subjects

29. Conclusions Compared to previous experiments, the average scores of subjects in this experiment were considerably higher. Subjects who play orchestral instruments tended to score higher than those who do not. The short-term training sessions had a significant effect on the subjects ’ performance for the 27-instrument test only. The excellent average score of the human subjects in this experiment presents new challenges for timbre-recognition computer models.