Statistical Analysis of Musical Sound Features Derived from Wavelet Representation

Kostek B, Zwan P, Dziubinski M. Statistical Analysis of Musical Sound Features Derived from Wavelet Representation. In: AES Convention 112. Audio Engineering Society; 2002. Paper 5523. Available from: https://aes.org/publications/elibrary-page/?id=11430

@inproceedings{Kostek2002_11430,
  author = {Kostek, Bozena and Zwan, Pawel and Dziubinski, Marek},
  title = {{Statistical Analysis of Musical Sound Features Derived from Wavelet Representation}},
  booktitle = {AES Convention 112},
  note = {Paper 5523},
  year = {2002},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=11430}
}

TY  - CPAPER
TI  - Statistical Analysis of Musical Sound Features Derived from Wavelet Representation
AU  - Kostek, Bozena
AU  - Zwan, Pawel
AU  - Dziubinski, Marek
T2  - AES Convention 112
M1  - Paper 5523
PY  - 2002
DA  - 2002/04/06
UR  - https://aes.org/publications/elibrary-page/?id=11430
PB  - Audio Engineering Society
LA  - en
AB  - The presented study is aimed to extract parameters from musical sounds that can be useful in the musical sound recognition process. For this purpose time-frequency transform analysis employing various filters is performed on musical sounds representing twelve instrument classes. Three groups of instruments are taken into account, namely: wind, string and percussive. Examples of wavelet analyses of various musical instrument sounds are presented. On this basis a number of parameters was extracted and statistically analyzed. Parameters that are correlated are removed from the feature vector. In this way a number of parameters in the feature vector can be diminished from dozens to a few most important ones. Then feature vectors were fed to the Artificial Neural Network inputs and classification experiments were performed. Furthermore originally developed Frequency Envelope Distribution method was applied to divide musical signal into harmonic and inharmonic content. Those signals were also parameterized and used in recognition experiments. Some experiment results are presented. The derived conclusions are also included in the paper.
ER  -