Sigmoidal NMFD: Convolutional NMF with Saturating Activations for Drum Mixture Decomposition

In many types of music, percussion plays an essential role to establish the rhythm and the groove of the music. Algorithms that can decompose the percussive signal into its constituent components would therefore be very useful, as they would enable many analytical and creative applications. This paper describes a method for the unsupervised decomposition of percussive recordings, building on the non-negative matrix factor deconvolution (NMFD) algorithm. Given a percussive music recording, NMFD discovers a dictionary of time-varying spectral templates and corresponding activation functions, representing its constituent sounds and their positions in the mix. We observe, however, that the activation functions discovered using NMFD do not show the expected impulse-like behavior for percussive instruments. We therefore enforce this behavior by specifying that the activations should take on binary values: either an instrument is hit, or it is not. To this end, we rewrite the activations as the output of a sigmoidal function, multiplied with a per-component amplitude factor. We furthermore define a regularization term that biases the decomposition to solutions with saturated activations, leading to the desired binary behavior. We evaluate several optimization strategies and techniques that are designed to avoid poor local minima. We show that incentivizing the activations to be binary indeed leads to the desired impulse-like behavior, and that the resulting components are better separated, leading to more interpretable decompositions.

“Beat de Drum and de Spirit Gon Get Up”

This chapter examines how the repertoire, form, content, and performance styles of traditional kweh-kweh songs and dances are performed and innovated at Come to My Kwe-Kwe to entertain, instruct, and educate the African-Guyanese diaspora in New York City. Accompanied by “found” instruments, synthesizers, djembes, and an assortment of percussive instruments, attendees sing traditional kweh-kweh songs, Guyanese folk songs, and musical genres from around the world. They sing using coded language, double-entendre, and unmasked (raw) speech to edify the community and facilitate inclusion. As attendees sing and dance in the ganda (performance space), they address diverse matrimonial topics, particularly sex. In fact, the volunteer bride and groom are expected to wine (gyrate) to demonstrate sexual prowess, or risk ridicule from the larger community. Some African-Guyanese-Americans disapprove of the musical innovations at Come to My Kwe-Kwe, but others view the changes as crucial to the survival of the ritual and the African-Guyanese community.

Extended Playing Techniques on an Augmented Virtual Percussion Instrument

Innovation and tradition are two fundamental factors in the design of new digital musical instruments. Although apparently mutually exclusive, novelty does not imply a total disconnection from what we have inherited from hundreds of years of traditional design, and the balance of these two factors often determines the overall quality of an instrument. Inspired by this rationale, in this article we introduce the Hyper Drumhead, a novel augmented virtual instrument whose design is deeply rooted in traditional musical paradigms, yet aimed at the exploration of unprecedented sounds and control. In the first part of the article we analyze the concepts of designing an augmented virtual instrument, explaining their connection with the practice of augmenting traditional instruments. Then we describe the design of the Hyper Drumhead in detail, focusing on its innovative physical modeling implementation. The finite-difference time-domain solver that we use runs on the parallel cores of a commercially available graphics card and permits the simulation of real-time 2-D wave propagation in massively sized domains. Thanks to the modularity of this implementation, musicians can create several 2-D virtual percussive instruments that support realistic playing techniques but whose affordances can be enhanced beyond most of the limits of traditional augmentation.

Dynamic Convolution Modeling, a Hybrid Synthesis Strategy

This article outlines a hybrid approach to the synthesis of percussion sounds. The synthesis method described here combines techniques and concepts from physical modeling and convolution to produce audio synthesis of percussive instruments. This synthesis method not only achieves a high degree of realism in comparison with audio samples but also retains some of the flexibility associated with waveguide physical models. When the results are analyzed, the method exhibits some interesting detailed spectral features that have some aspects in common with the behavior of acoustic percussion instruments. In addition to outlining the synthesis process, the article discusses some of the more creative possibilities inherent in this approach, e.g., the use and free combination of excitation and resonance sources from beyond the realms of the purely percussive examples given.

Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation

Recently, shift-invariant tensor factorisation algorithms have been proposed for the purposes of sound source separation of pitched musical instruments. However, in practice, existing algorithms require the use of log-frequency spectrograms to allow shift invariance in frequency which causes problems when attempting to resynthesise the separated sources. Further, it is difficult to impose harmonicity constraints on the recovered basis functions. This paper proposes a new additive synthesis-based approach which allows the use of linear-frequency spectrograms as well as imposing strict harmonic constraints, resulting in an improved model. Further, these additional constraints allow the addition of a source filter model to the factorisation framework, and an extended model which is capable of separating mixtures of pitched and percussive instruments simultaneously.