Automatic Classification of Musical Audio Signals Employing Machine Learning Approach

Zwan P, Kostek B, Kupryjanow A. Automatic Classification of Musical Audio Signals Employing Machine Learning Approach. In: AES Convention 130. Audio Engineering Society; 2011. Paper 8449. Available from: https://aes.org/publications/elibrary-page/?id=15916

@inproceedings{Zwan2011_15916,
  author = {Zwan, Pawel and Kostek, Bozena and Kupryjanow, Adam},
  title = {{Automatic Classification of Musical Audio Signals Employing Machine Learning Approach}},
  booktitle = {AES Convention 130},
  note = {Paper 8449},
  year = {2011},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15916}
}

TY  - CPAPER
TI  - Automatic Classification of Musical Audio Signals Employing Machine Learning Approach
AU  - Zwan, Pawel
AU  - Kostek, Bozena
AU  - Kupryjanow, Adam
T2  - AES Convention 130
M1  - Paper 8449
PY  - 2011
DA  - 2011/05/06
UR  - https://aes.org/publications/elibrary-page/?id=15916
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents a thorough analysis of automatic classification applied to musical audio signals. The classification is based on a chosen set of machine learning algorithms. A database of 60 music composers/performers was prepared for the purpose of the described research. For each of the musicians, 15-20 music pieces were collected. All the pieces were partitioned into 20 segments and then parameterized. The feature vector consisted of 171 parameters, including MPEG-7 low-level descriptors and mel-frequency cepstral coefficients (MFCC) complemented with time-related dedicated parameters. The task of the classifier was to recognize the composer/performer and to properly categorize a selected piece of music. The paper also presents and discusses the results of classification.
ER  -