Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015 COMPUTATIONAL STRATEGIES FOR BREAKBEAT CLASSIFICATION AND RESEQUENCING IN HARDCORE, JUNGLE AND DRUM & BASS Jason A. Hockman, Matthew E. P. Davies∗ DMT Lab, Sound and Music Computing Group, Birmingham City University INESC TEC Birmingham, United Kingdom Porto, Portugal [email protected] [email protected] ABSTRACT associated with segments of the breakbeat within a sequencer— also termed resequencing. Breakbeat usage in HJDB centres on The dance music genres of hardcore, jungle and drum & bass the creative transformation of an initial source breakbeat through (HJDB) emerged in the United Kingdom during the early 1990s any number of techniques available to HJDB producers. These as a result of affordable consumer sampling technology and the transformations may include pitch-shifting, time-stretching, filter- popularity of rave music and culture. A key attribute of these gen- ing, resampling, and resequencing. If the undertaken process is res is their usage of fast-paced drums known as breakbeats. Auto- viewed as a pipeline with the original source breakbeat as the au- mated analysis of breakbeat usage in HJDB would allow for novel dio input and the transformed breakbeat as the output, we can digital audio effects and musicological investigation of the genres. consider a breakbeat transformation to be a kind of complex au- An obstacle in this regard is the automated identification of break- dio effect. HJDB producers (e.g., Bay B Kane, Justice, DJ Krust) beats used in HJDB music. This paper compares three strategies are well known for their choice of source breakbeat material and for breakbeat detection: (1) a generalised frame-based music clas- recognisable for the types of transformations they employ. More sification scheme; (2) a specialised system that segments drums recently, artists such as Fracture and Om Unit have led a resur- from the audio signal and labels them with an SVM classifier; (3) gence in the popularity of using breakbeats, and continue to re- an alternative specialised approach using a deep network classifier. define the aesthetic of breakbeat manipulation. Hardcore, jungle The results of our evaluations demonstrate the superiority of the and drum & bass exist as genres within a continuum of musical specialised approaches, and highlight the need for style-specific influence that involves the cultural and generational borrowing of workflows in the determination of particular musical attributes in stylistic cues and sonic artefacts (i.e., breakbeat samples) [1], sim- idiosyncratic genres. We then leverage the output of the break- ilar to North America’s hip hop, which also relies on the history beat classification system to produce an automated breakbeat se- and sound palette of funk and jazz. quence reconstruction, ultimately recreating the HJDB percussion arrangement. 1.2. Motivation 1. INTRODUCTION Automated analysis of breakbeat usage is of interest both from a 1.1. Background musicological perspective—to gain insight on a technology-driven composition process—as well as in terms of how to recreate the During the early 1990s, DJ-oriented electronic musicians in the stylistic idiom of HJDB producers. Analysis of rhythmic modifica- United Kingdom embraced affordable sampling technologies (e.g., tion of breakbeats can provide insight into the resequencing prac- Akai S950), allowing them to replicate and manipulate recorded tices undertaken by HJDB producers in individual tracks, which sounds without the need for reverse engineering or synthesis. These could, in turn, be used to assess these practices across an artist’s technologies, and the innovative techniques developed to harness career or across subgenres. Automating this procedure would be these technologies resulted in the development of three new gen- useful to music producers in search of novel drum sequences for res: hardcore, jungle and drum & bass (HJDB). A unique attribute use in their productions, or as an educative recommendation tool of these genres is their integration of short segments of percussion for musicians to learn how others have structured their own per- solos from funk and jazz recordings known as breakbeats. cussion arrangements. While melody and harmony are often-used attributes in the de- In pursuit of both these goals, we explore techniques for the termination of an artist’s ability within many genres, perhaps the automatic recognition of breakbeats as a critical first step towards most revealing characteristic by which an HJDB producer, track understanding how they have been used and repurposed within (herein used to describe a complete musical piece) or subgenre HJDB. Identification of source samples has become a popular on- might be individuated, is through breakbeat selection and creative line activity, and the ability to “spot” samples is considered a mark usage of breakbeats. Breakbeat selection is the choice of one or of a good sample-based musician. Whosampled is an online com- more breakbeats taken from the vast amount of existing funk and munity of musicians and avid listeners that collectively attempts jazz recordings. Breakbeats are typically recorded into a sampler’s to document information related to sample usage.1 The site al- memory, and an arrangement is created by reordering MIDI notes lows users to explore how sampled music has been appropriated ∗ MD is financed by National Funds through the FCT - Fundação para a Ciência e a Tecnologia within post-doctoral grant SFRH/BPD/88722/2012. 1www.whosampled.com DAFX-1 Proc. of the 18th Int. Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, Nov 30 - Dec 3, 2015 by other musicians and to provide links between new and old ma- HJDB terial. Audio At present there are only a handful of tools that have been presented that seek to automate the sample identification process. Percussion Downbeat Balen et al. [2] adapted the audio fingerprinting method of Wang Separation Detection spectral peak Breakbeat [3] to the task of sample identification. Here the or Resequencing landmark method is used to identify a series of salient peaks in the Drum Analysis breakbeat spectrogram that are similar to those in other recordings. While Detection arrangement the spectral peak method has been shown to be robust to noise and Breakbeat distortion, it is less useful for the detection of percussion, or in Feature Feature Classification Reduction the context of heavily transposed, or pitched material [2], which Extraction is often the case with breakbeats. Alternatively, Dittmar presents a sample plagiarism tool designed to assess similarity between musical excerpts by evaluating the correlation between the time- Figure 1: Overview of proposed breakbeat resequencing analy- varying gains representing basis activations as generated through sis method with specialised processing. HJDB audio enters into non-negative matrix factorisation [4]. Whitney adapted Dittmar’s stages of the breakbeat classification system (black boxes). Addi- model to assess similarity using Pearson’s correlation [5]. tional processing stages (white boxes) are undertaken to achieve In this paper we investigate two classification scenarios for the breakbeat rearrangement. determining the presence of breakbeats in music that has incorpo- rated them. First, we attempt to identify tracks that use a given breakbeat within a dataset with a limited set of known labels to or may only appear as brief segments—that is, not in their entirety. determine the general validity of the approach under controlled We therefore propose a specialised solution, which extracts and conditions. Second, we attempt a more realistic formalisation as a classifies individual drums from the musical timeline using a com- binary classification problem directed towards the presence or ab- bined signal processing and machine learning approach. Our for- sence of a given breakbeat within a database of HJDB music with malisation is motivated by the actual breakbeat usage in HJDB mu- a much larger number of breakbeats. Following this classification sic, in which segments of multiple breakbeats may be used within stage, we then automatically extract rhythmic and metrical infor- the same track. mation in the form of onset and downbeat locations to facilitate the segmentation of breakbeats and hence the eventual reverse en- 2.1. Multi-breakbeat Classification gineering to relate the output (i.e., transformed breakbeat) back to the source input. The first breakbeat classification method attempts to select the un- We consider the presented techniques to be part of computa- derlying breakbeat from a set of known breakbeat classes in a tional research in DJ-oriented electronic music, much of which has multi-class classification problem; for example, to determine if the been pioneered by Collins [6, 7, 8], who developed the bbcut real- breakbeat in an HJDB track is of Amen,2 Funky Mule,3 or Apache 4 time breakbeat segmentation and resequencing tools intended to origin. We apply this design as a simplified problem to test the va- replicate the idiomatic breakbeat manipulations of the genre. lidity of the proposed model, however it is also a necessary formal- The remainder of this paper is structured as follows: Section 2 isation for solving ties in the event that more than one breakbeat outlines our breakbeat classification method. Section 3 presents has been selected in a series of harder binary classification prob- our evaluations and datasets. Section 4 presents our proposed lems (e.g., Amen versus non-Amen, where non-Amen is comprised method for breakbeat sequence reconstruction and reproduction of examples with any number of breakbeats present). that utilises the breakbeat classification performed in Section 2. Within the above context, we first concentrate our efforts on Finally, Section 5 provides conclusions and areas for future work. the detection and classification of bass drums as they are the drum class within the standard drum kit that tend to exhibit the least amount of timbral overlap with other sounds. Snare drums in com- 2. METHOD parison tend to exhibit more overlap with other sounds as they extend over a wider range of frequencies.