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Overview Breakbeat Classification Evaluation Breakbeat Resequencing

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Overview Breakbeat Classification Evaluation Breakbeat Resequencing Computational strategies for breakbeat classification and resequencing in Hardcore, Jungle and Drum & Bass Jason A. Hockman, Birmingham City Unversity, United Kingdom [email protected] Matthew E. P. Davies, INESC TEC, Portugal [email protected] Overview HJDB Breakbeats are short segments of percussion solos sampled from jazz and Audio funk recordings of the 1960s–80s that form an integral part of the dance Perc u ss io n Downb ea t music genres of hardcore, jungle and drum & bass (HJDB) Se p ara tio n Det ec t io n Breakbeat Resequencing Producers of HJDB subject these breakbeats to substantial processing Drum Analysis Breakbeat Detection including time-stretching, pitch-shifting, and re-ordering for creative effect Arrangement Breakb ea t Fea t u r e Fea t u r e Cl ass ifi ca t ion Our goal is first to automatically identify which breakbeat has been sampled Redu c t io n and then to discover how it was re-ordered, towards automatic resequencing Extraction Input waveform Breakbeat classification 1 e 0.5 We explore two approaches: 0 0.5 • multi-breakbeat classification: choosing between a set of 3 well-known breakbeats Amplitud 1 1 2 3 4 5 6 Amen Apache Funky Mule , , and Bass and snare onset detection functions 100 e • binary classification: determining the presence of a single breakbeat (e.g., Amen ) 50 For both approaches we first identify the locations of bass drum events using methods Amplitud 0 from [1,2] and then extract a set of spectral bass drum features (B) and timbral 0 1 2 3 4 5 6 ) Spectral features at detected bass drum onsets features MFCCs (M) and perform dimensionality reduction using PCA. Features are then 300 passed to a classification stage to determine the breakbeat being used. For multi-class 200 classification we compare GMM and SVM classifiers, and for binary classification we 100 Frequency (Hz Frequency compare the best-performing multi-class method with a deep neural network [3] 0 1 2 3 4 5 6 Time (s) Evaluation Multi-breakbeat classification M-GMM M-SVM B-SVM BM-SVM Amen, Apache, For multi-breakbeat classification we use 93 excerpts (31 per class: Amen 74.2% 80.6% 61.3% 83.9% Funky Mule) of duration between 15 seconds and 2 minutes. We test four Apache 74.2% 67.7% 77.4% 90.3% configurations: Funky Mule 41.9% 54.8% 93.5% 87.1% • M-GMM: MFCC-based features with a GMM classifer • M-SVM: MFCC-based features with an SVM classifier Avg. 63.4% 67.7% 77.4% 87.1% • B-SVM: Bass drum spectral features with an SVM classifier • BM-SVM: Bass drum spectral features and MFCCs with an SVM classifer Binary classification BM-SVM BM-DN Amen Non-Amen For binary classification we use 270 excerpts (148 and 132 ) Amen 78.4% 81.1% and test two configurations: Non - Amen 77.1% 86.1% • BM-SVM: The best performing method for multi-breakbeat classification Avg. 77.8% 83.6% • BM-DN : A deep neural network trained on the same features (without PCA) Breakbeat resequencing Once we have identified the original breakbeat, we can use this HJDB measur e 1 information along with the estimated metrical structure [4] to K1 H3 S1 H4 K1 H2 S2 H4 attempt to find the mapping between the modified breakbeat and the original This technique can be used for computational musicology to aid K1 H1 S1 H2 H3 K2 S2 H4 K3 S3 in the analysis of the compositional processes of DJ-oriented Br eakbeat measu re 1 Br eakbeat measu re 2 electronic music producers, and furthermore to enable creative musical applications by learning how musicians resequence breakbeats References [1] D. Fitzgerald, 2010. "Harmonic/percussive separation using median filtering," in Proceedings of the 13th International Conference on Digital Audio Effects, pp. 203–6. [2] M.E.P. Davies, P. Hamel, K. Yoshii, and M. Goto, 2014. "Automashupper: Automatic creation of multi-song music mashups," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 12, pp. 1726–37. [3] F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. Goodfellow, A. Bergeron, N. Bouchard, D. Warde-Farley, and Y. Bengio, 2012. "Theano: New features and speed improvements," in Proceedings of the NIPS 2012 Deep Learning Workshop. [4] J.A. Hockman, M.E.P. Davies, and I. Fujinaga, 2012. "One in the jungle: Downbeat detection in hardcore, jungle, and drum & bass," in Proceedings of the 13th International Society of Music Information Retrieval Conference, pp. 169–74. MD is financed by National Funds through the FCT - Fundação para a Ciência e a Tecnologia within post-doctoral grant SFRH/BPD/88722/2012.
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