Daniel McEnnis

Masters Student in Music Technology at McGill Univeristy


Past Projects

proof command
collaborative filtering
Java Effects
Current Research
Digital Libraries
Digital Library Comparsions
Thesis Proposal
jAudio Javadoc
System Administration
bit-for-bit backups
Restoring a System
Network Speed Tests
MUMT 611 Links
Meldex Presentation
MPEG1 Presentation
Timbre Presentation
SOAP Presentation
FeatureExtraction Project

Timbre Similarity



Annotated Bibliography

  • Brown, J. 1999. Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. Journal of the Acoustical Society of America. 105: 1933–41.

    Paper by a physicist - not in musical circles. Used 1 single long recording to train sax and oboe sounds. Continually retried which sample to pick on an ad hoc basis. Used Gaussian classifier to classify set.

  • Fujinaga, I. 1998. Machine recognition of timbre using steady-state tone of acoustic musical instruments. Proceedings of the International Computer Music Conference. 207–10.

    Utilizes hand picked steady state segments of McGill instrument samples. Uses GA and KNN to classify.

  • Fujinaga, I. and K. MacMillan. 2000. Realtime recognition of orchestral instruments. Proceedings of the International Computer Music Conference. 141–3.

    Realtime extension of Fujinaga's 1998 paper. Realtime. Utilizes PD for analysis and GA+KNN for classification.

  • Grey, J., and G. Gordon. 1978. Perceptual effects of spectral modifications on musical timbres. Journal of the Acoustical Society of America. 63(5): 1493-1500.

    First paper on timbre similarity. Utilized pairwise similarity ratings of 16 tones and their simplified versions. Used MDS to conclude that timbre has 3 dimensions. Accounts spectral centroid as 1 dimension.

  • Herrera, P., A. Yeterian, and F. Gouyon. 2002. Automatic classification of drum sounds: A comparison of feature selection methods and classification techniques. International Conference on Music and Artificial Intelligence. 2: 69-80.

    Used sample of 634 drum samples from different sample cds. Split features between attack time and decay time. Only MFCC and empirically derived energy bands cover the entire signal.

  • Tindale, A., A. Kapur, G. Tzanetakis, and I. Fujinaga. 2004. Retrieval of percussion gestures using timbre classification techniques. Proceedings of the International Conference on Music Information Retrieval. 541-5.

    Classificaion of different types of snares. Used 3 different classifiers - KNN, SVM, and Neural Nets.