slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Recognition of Isolated Instrument Tones PowerPoint Presentation
Download Presentation
Recognition of Isolated Instrument Tones

Loading in 2 Seconds...

play fullscreen
1 / 27
Download Presentation

Recognition of Isolated Instrument Tones - PowerPoint PPT Presentation

Download Presentation

Recognition of Isolated Instrument Tones

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

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

    Slide 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.

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

    Slide 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.

    Slide 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.

    Slide 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

    Slide 8:List of Instruments

    Slide 9:List of Instruments

    Slide 10:Previous experiments and Peabody

    Slide 11:Recognition rates for previous human experiments

    Slide 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

    Slide 13:Recognition rates for previous human experiments and Peabody results

    Slide 14:Previous computer experiments

    Slide 15:Recognition rates for previous computer and human experiments and Peabody

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

    Slide 17:Confusion matrix (9-instr.)

    Slide 18:Confusion matrix (3D-View)

    Slide 19:Confusion matrix comparison

    Slide 20:Confusion matrix (27-instr.)

    Slide 21:Confusion matrix (3D-View)

    Slide 22:Confusion matrix (Martin)

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

    Slide 24:Confusion matrix comparison

    Slide 25:Family vs. Exact Answers

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

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

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

    Slide 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.