An Evaluation of Pre-Processing Algorithms for Rhythmic Pattern Analysis
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M. Gruhne, C. Dittmar, D. Gaertner, and G. Schuller, "An Evaluation of Pre-Processing Algorithms for Rhythmic Pattern Analysis," Paper 7542, (2008 October.). doi:
M. Gruhne, C. Dittmar, D. Gaertner, and G. Schuller, "An Evaluation of Pre-Processing Algorithms for Rhythmic Pattern Analysis," Paper 7542, (2008 October.). doi:
Abstract: For the semantic analysis of polyphonic music, such as genre recognition, rhythmic pattern features (also called Beat Histogram) can be used. Feature extraction is based on the correlation of rhythmic information from drum instruments in the audio signal. In addition to drum instruments, the sounds of pitched instruments are usually also part of the music signal to analyze. This can have a significant influence on the correlation patterns. This paper describes the influence of pitched instruments for the extraction of rhythmic features, and evaluates two different pre-processing methods. One method computes a sinusoidal and noise model, where its residual signal is used for feature extraction. In the second method, a drum transcription based on spectral characteristics of drum sounds is performed, and the rhythm pattern feature is derived directly from the occurrences of the drum events. Finally, the results are explained and compared in detail.
@article{gruhne2008an,
author={gruhne, matthias and dittmar, christian and gaertner, daniel and schuller, gerald},
journal={journal of the audio engineering society},
title={an evaluation of pre-processing algorithms for rhythmic pattern analysis},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{gruhne2008an,
author={gruhne, matthias and dittmar, christian and gaertner, daniel and schuller, gerald},
journal={journal of the audio engineering society},
title={an evaluation of pre-processing algorithms for rhythmic pattern analysis},
year={2008},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={for the semantic analysis of polyphonic music, such as genre recognition, rhythmic pattern features (also called beat histogram) can be used. feature extraction is based on the correlation of rhythmic information from drum instruments in the audio signal. in addition to drum instruments, the sounds of pitched instruments are usually also part of the music signal to analyze. this can have a significant influence on the correlation patterns. this paper describes the influence of pitched instruments for the extraction of rhythmic features, and evaluates two different pre-processing methods. one method computes a sinusoidal and noise model, where its residual signal is used for feature extraction. in the second method, a drum transcription based on spectral characteristics of drum sounds is performed, and the rhythm pattern feature is derived directly from the occurrences of the drum events. finally, the results are explained and compared in detail.},}
TY - paper
TI - An Evaluation of Pre-Processing Algorithms for Rhythmic Pattern Analysis
SP -
EP -
AU - Gruhne, Matthias
AU - Dittmar, Christian
AU - Gaertner, Daniel
AU - Schuller, Gerald
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008
TY - paper
TI - An Evaluation of Pre-Processing Algorithms for Rhythmic Pattern Analysis
SP -
EP -
AU - Gruhne, Matthias
AU - Dittmar, Christian
AU - Gaertner, Daniel
AU - Schuller, Gerald
PY - 2008
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2008
AB - For the semantic analysis of polyphonic music, such as genre recognition, rhythmic pattern features (also called Beat Histogram) can be used. Feature extraction is based on the correlation of rhythmic information from drum instruments in the audio signal. In addition to drum instruments, the sounds of pitched instruments are usually also part of the music signal to analyze. This can have a significant influence on the correlation patterns. This paper describes the influence of pitched instruments for the extraction of rhythmic features, and evaluates two different pre-processing methods. One method computes a sinusoidal and noise model, where its residual signal is used for feature extraction. In the second method, a drum transcription based on spectral characteristics of drum sounds is performed, and the rhythm pattern feature is derived directly from the occurrences of the drum events. Finally, the results are explained and compared in detail.
For the semantic analysis of polyphonic music, such as genre recognition, rhythmic pattern features (also called Beat Histogram) can be used. Feature extraction is based on the correlation of rhythmic information from drum instruments in the audio signal. In addition to drum instruments, the sounds of pitched instruments are usually also part of the music signal to analyze. This can have a significant influence on the correlation patterns. This paper describes the influence of pitched instruments for the extraction of rhythmic features, and evaluates two different pre-processing methods. One method computes a sinusoidal and noise model, where its residual signal is used for feature extraction. In the second method, a drum transcription based on spectral characteristics of drum sounds is performed, and the rhythm pattern feature is derived directly from the occurrences of the drum events. Finally, the results are explained and compared in detail.
