Automated classification of piano-guitar notes

In this paper, a new decisively important factor in both the perceptual and the automated piano-guitar identification process is introduced. This factor is determined by the nontonal spectral content of a note, while it is, in practice, totally independent of the note spectrum tonal part. This conclusion and all related results are based on a number of extended acoustical experiments, performed over the full pitch range of each instrument. The notes have been recorded from six different performers each of whom played a different instrument. Next, a number of powerful criteria for the classification between guitar and piano is proposed. Using these criteria, automated classification between 754 piano and guitar test notes has been achieved with a 100% success rate.

[1]  J. Grey.  Multidimensional perceptual scaling of musical timbres. , 1977, The Journal of the Acoustical Society of America.

[2]  K. Berger.  Some Factors in the Recognition of Timbre , 1964 .

[3]  Ian Kaminskyj,et al.  Automatic source identification of monophonic musical instrument sounds , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Melville Clark,et al.  A Preliminary Experiment on the Perceptual Basis for Musical Instrument Families , 1964 .

[5]  William J. Strong,et al.  Perturbations of Synthetic Orchestral Wind‐Instrument Tones , 1967 .

[6]  P. Iverson,et al.  Measuring similarity of musical timbres , 1991 .

[7]  J. W. Gordon,et al.  Perceptual effects of spectral modifications on musical timbres , 1978 .

[8]  Paolo Tonella,et al.  Self-organizing Neural Network and Grey's Timbre Space , 1993, ICMC.

[9]  Keith Dana Martin,et al.  Sound-source recognition: a theory and computational model , 1999 .

[10]  J. Grey,et al.  Perceptual evaluations of synthesized musical instrument tones , 1977 .

[11]  L. Wedin,et al.  Dimension analysis of the perception of instrumental timbre. , 1972, Scandinavian journal of psychology.

[12]  Xavier Serra,et al.  Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques , 2000, ISMIR.

[13]  Youngmoo E. Kim,et al.  Musical instrument identification: A pattern‐recognition approach , 1998 .

[14]  Xavier Serra,et al.  Musical Sound Modeling with Sinusoids plus Noise , 1997 .

[15]  G. Soete,et al.  Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes , 1995, Psychological research.

[16]  W. Strong,et al.  Synthesis of Wind‐Instrument Tones , 1967 .

[17]  J. Smith,et al.  A Sound Decomposition System Based on a Deterministic plus Residual Model , 1990 .

[18]  J C Brown.  Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. , 1999, The Journal of the Acoustical Society of America.