Evaluation of Drum Rhythmspace in a Music Production Environment

In modern computer-based music production, vast musical data libraries are essential. However, their presentation via subpar interfaces can hinder creativity, complicating the selection of ideal sequences. While low-dimensional space solutions have been suggested, their evaluations in real-world music production remain limited. In this study, we focus on Rhythmspace, a two-dimensional platform tailored for the exploration and generation of drum patterns in symbolic MIDI format. Our primary objectives encompass two main aspects: first, the evolution of Rhythmspace into a VST tool specifically designed for music production settings, and second, a thorough evaluation of this tool to ascertain its performance and applicability within the music production scenario. The tool’s development necessitated transitioning the existing Rhythmspace, which operates in Puredata and Python, into a VST compatible with Digital Audio Workstations (DAWs) using the JUCE(C++) framework. Our evaluation encompassed a series of experiments, starting with a composition test where participants crafted drum sequences followed by a listening test, wherein participants ranked the sequences from the initial experiment. The results show that Rhythmspace and similar tools are beneficial, facilitating the exploration and creation of drum patterns in a user-friendly and intuitive manner, and enhancing the creative process for music producers. These tools not only streamline the drum sequence generation but also offer a fresh perspective, often serving as a source of inspiration in the dynamic realm of electronic music production

Automatic characterization and generation of music loops and instrument samples for electronic music production

Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation

Automatic Drum Transcription and Source Separation

While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments

Automated Rhythmic Transformation of Drum Recordings

Within the creative industries, music information retrieval techniques are now being applied in a variety of music creation and production applications. Audio artists incorporate techniques from music informatics and machine learning (e.g., beat and metre detection) for generative content creation and manipulation systems within the music production setting. Here musicians, desiring a certain sound or aesthetic influenced by the style of artists they admire, may change or replace the rhythmic pattern and sound characteristics (i.e., timbre) of drums in their recordings with those from an idealised recording (e.g., in processes of redrumming and mashup creation). Automated transformation systems for rhythm and timbre can be powerful tools for music producers, allowing them to quickly and easily adjust the different elements of a drum recording to fit the overall style of a song. The aim of this thesis is to develop systems for automated transformation of rhythmic patterns of drum recordings using a subset of techniques from deep learning called deep generative models (DGM) for neural audio synthesis. DGMs such as autoencoders and generative adversarial networks have been shown to be effective for transforming musical signals in a variety of genres as well as for learning the underlying structure of datasets for generation of new audio examples. To this end, modular deep learning-based systems are presented in this thesis with evaluations which measure the extent of the rhythmic modifications generated by different modes of transformation, which include audio style transfer, drum translation and latent space manipulation. The evaluation results underscore both the strengths and constraints of DGMs for transformation of rhythmic patterns as well as neural synthesis of drum sounds within a variety of musical genres. New audio style transfer (AST) functions were specifically designed for mashup-oriented drum recording transformation. The designed loss objectives lowered the computational demands of the AST algorithm and offered rhythmic transformation capabilities which adhere to a larger rhythmic structure of the input to generate music that is both creative and realistic. To extend the transformation possibilities of DGMs, systems based on adversarial autoencoders (AAE) were proposed for drum translation and continuous rhythmic transformation of bar-length patterns. The evaluations which investigated the lower dimensional representations of the latent space of the proposed system based on AAEs with a Gaussian mixture prior (AAE-GM) highlighted the importance of the structure of the disentangled latent distributions of AAE-GM. Furthermore, the proposed system demonstrated improved performance, as evidenced by higher reconstruction metrics, when compared to traditional autoencoder models. This implies that the system can more accurately recreate complex drum sounds, ensuring that the produced rhythmic transformation maintains richness of the source material. For music producers, this means heightened fidelity in drum synthesis and the potential for more expressive and varied drum tracks, enhancing the creativity in music production. This work also enhances neural drum synthesis by introducing a new, diverse dataset of kick, snare, and hi-hat drum samples, along with multiple drum loop datasets for model training and evaluation. Overall, the work in this thesis increased the profile of the field and hopefully will attract more attention and resources to the area, which will help drive future research and development of neural rhythmic transformation systems

Drum translation for timbral and rhythmic transformation

Many recent approaches to creative transformations of musical audio have been motivated by the success of raw audio generation models such as WaveNet, in which audio samples are modeled by generative neural networks. This paper describes a generative audio synthesis model for multi-drum translation based on a WaveNet denosing autoencoder architecture. The timbre of an arbitrary source audio input is transformed to sound as if it were played by various percussive instruments while preserving its rhythmic structure. Two evaluations of the transformations are conducted based on the capacity of the model to preserve the rhythmic patterns of the input and the audio quality as it relates to timbre of the target drum domain. The first evaluation measures the rhythmic similarities between the source audio and the corresponding drum translations, and the second provides a numerical analysis of the quality of the synthesised audio. Additionally, a semi- and fully-automatic audio effect has been proposed, in which the user may assist the system by manually labelling source audio segments or use a state-of-the-art automatic drum transcription system prior to drum translation

Cooperation Between Top-Down and Low-Level Markov Chains for Generating Rock Drumming

Without heavy modification, the Markov chain is insufficient to handle the task of generating rock drum parts. This paper proposes a system for generating rock drumming that involves the cooperation between a top - down Markov chain that determi nes the structure of created drum parts and a low - level Markov chain that determines their contents. The goal of this system is to generate verse - or chorus - length drum parts that sound reminiscent of the drumming on its input pieces

Triple Synthesis

This thesis investigates the result of merging three musical approaches (jazz fusion, breakbeat/IDM and Electronic Dance Music) and their respective methodologies as applied to music composition. It is presented in a progressive manner. Chapters two to four identify and discuss each of the three styles separately in terms of the research undertaken in the preparation of this thesis. Chapter 2 discusses, through a close examination of selected compositions and recordings, both Weather Report and Herbie Hancock as representing source material for research and compositional study in terms of melody, harmony and orchestration from the 1970s jazz-fusion genre. Chapter 3 examines breakbeat and Intelligent Dance Music (IDM) drum rhythm programming through both technique and musical application. Chapter 4 presents an examination of selected contemporary Electronic Dance Music (EDM) techniques and discusses their importance in current electronic music styles. Chapters 5, 6 and 7 each present an original composition based on the application and synthesis of the styles and techniques explored in the previous three chapters, with each composition defined by proportions of influence from each of the three styles as in the Venn diagram shown in the introduction. Since the musical context of the original compositions is software oriented, diagrams and computer screenshots are used in addition to conventional score notation in order to highlight details of musical examples and techniques. The final chapter discusses the conclusions made through the thesis research and result of this “synthesis” style of composition

Data-Driven Sound Track Generation

Background music is often used to generate a specific atmosphere or to draw our attention to specific events. For example in movies or computer games it is often the accompanying music that conveys the emotional state of a scene and plays an important role for immersing the viewer or player into the virtual environment. In view of home-made videos, slide shows, and other consumer-generated visual media streams, there is a need for computer-assisted tools that allow users to generate aesthetically appealing music tracks in an easy and intuitive way. In this contribution, we consider a data-driven scenario where the musical raw material is given in form of a database containing a variety of audio recordings. Then, for a given visual media stream, the task consists in identifying, manipulating, overlaying, concatenating, and blending suitable music clips to generate a music stream that satisfies certain constraints imposed by the visual data stream and by user specifications. It is our main goal to give an overview of various content-based music processing and retrieval techniques that become important in data-driven sound track generation. In particular, we sketch a general pipeline that highlights how the various techniques act together and come into play when generating musically plausible transitions between subsequent music clips

The Arpeggiator: A Compositional tool for Performance and Production

The properties of the arpeggiator bring forth a creative process that marries production, composition, improvisation and performance in a manner that inspires the musician/producer, helped define the aesthetics, creative process, and social function of electronic music as a whole, while grounding that music in an association with traditional African-American music and notions of futurism simultaneously. The arpeggiators impact on aesthetics is explored, demonstrating how automation and repetition combine to inject mechanical aesthetics into music, reflecting societys immersion and fascination with automation and futuristic technology while redefining the creative process of the musician. This paper establishes that the arpeggiator is more than just a series of knobs on a synthesizer that manipulate sound or act as a facilitator for performance. Rather, by referencing my creative process and compositions within the context of belonging to the lineage of African-American music, this paper will demonstrate how the arpeggiator is representational of electronic dance musics overall essential qualities