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 HJD B Breakbeats are short segments of percussion solos sampled from jazz and Audi o funk recordings of the 1960s–80s that form an integral part of the dance Perc uss ion Downb ea t

music genres of hardcore, jungle and drum & bass (HJDB) Se para tio n De tec tio n Breakbea t Resequencin g Producers of HJDB subject these breakbeats to substantial processing Drum Analysis Breakbea t

Detection including time-stretching, pitch-shifting, and re-ordering for creative effect Arrangement

Break bea t Fea tu r e Fea t ur e Cl ass ifica t ion Our goal is first to automatically identify which breakbeat has been sampled Redu c t ion

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 measu re 1 information along with the estimated metrical structure [4] to K 1 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 K 3 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