TY - JOUR
T1 - Music genre classification using MIDI and audio features
AU - Cataltepe, Zehra
AU - Yaslan, Yusuf
AU - Sonmez, Abdullah
PY - 2007
Y1 - 2007
N2 - We report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification. We use McKay and Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use normalized compression distance (NCD). NCD uses the compressed length of a string as an approximation to its Kolmogorov complexity and has previously been used for music genre and composer clustering. We convert the MIDI pieces to audio and then use the audio features to train different classifiers. MIDI and audio from MIDI classifiers alone achieve much smaller accuracies than those reported by McKay and Fujinaga who used not NCD but a number of domain-based MIDI features for their classification. Combining MIDI and audio from MIDI classifiers improves accuracy and gets closer to, but still worse, accuracies than McKay and Fujinaga's. The best root genre accuracies achieved using MIDI, audio, and combination of them are 0.75, 0.86, and 0.93, respectively, compared to 0.98 of McKay and Fujinaga. Successful classifier combination requires diversity of the base classifiers. We achieve diversity through using certain number of seconds of the MIDI file, different sample rates and sizes for the audio file, and different classification algorithms.
AB - We report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification. We use McKay and Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use normalized compression distance (NCD). NCD uses the compressed length of a string as an approximation to its Kolmogorov complexity and has previously been used for music genre and composer clustering. We convert the MIDI pieces to audio and then use the audio features to train different classifiers. MIDI and audio from MIDI classifiers alone achieve much smaller accuracies than those reported by McKay and Fujinaga who used not NCD but a number of domain-based MIDI features for their classification. Combining MIDI and audio from MIDI classifiers improves accuracy and gets closer to, but still worse, accuracies than McKay and Fujinaga's. The best root genre accuracies achieved using MIDI, audio, and combination of them are 0.75, 0.86, and 0.93, respectively, compared to 0.98 of McKay and Fujinaga. Successful classifier combination requires diversity of the base classifiers. We achieve diversity through using certain number of seconds of the MIDI file, different sample rates and sizes for the audio file, and different classification algorithms.
UR - http://www.scopus.com/inward/record.url?scp=33846842442&partnerID=8YFLogxK
U2 - 10.1155/2007/36409
DO - 10.1155/2007/36409
M3 - Article
AN - SCOPUS:33846842442
SN - 1110-8657
VL - 2007
JO - Eurasip Journal on Advances in Signal Processing
JF - Eurasip Journal on Advances in Signal Processing
M1 - 36409
ER -