Paper Archive

In this paper is presented the DESAM Toolbox, a set of Matlab functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processing tools that decompose music files according to different signal models, giving rise to different “mid-level” representations. After motivating the need for such a toolbox, this paper offers an overview of the overall organization of the toolbox, and describes all available functionalities.

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In this work the Fan Chirp Transform (FChT), which provides an acute representation of harmonically related linear chirp signals, is applied to the analysis of pitch content in polyphonic music. The implementation introduced was devised to be computationally manageable and enables the generalization of the FChT for the analysis of non-linear chirps. The combination with the Constant Q Transform is explored to build a multi-resolution FChT. An existing method to compute pitch salience from the FChT is improved and adapted to handle polyphonic music. In this way a useful melodic content visualization tool is obtained. The results of a frame based melody detection evaluation indicate that the introduced technique is very promising as a front-end for music analysis.

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Recently, Non-negative Matrix Factorisation (NMF) has found application in separation of individual sound sources. NMF decomposes the spectrogram of an audio mixture into an additive parts based representation where the parts typically correspond to individual notes or chords. However, there is a need to cluster the NMF basis functions to their sources. Although, many attempts have been made to improve the clustering of the basis functions to sources, much research is still required in this area. Recently, Shifted Non-negative Matrix Factorisation (SNMF) was used to cluster these basis functions. To this end, we propose that the incorporation of group sparsity to the Shifted NMF based methods may benefit the clustering algorithms. We have tested this on SNMF algorithms with improved separation quality. Results show that this gives improved clustering of pitched basis functions over previous methods.

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Real-time sound synthesis of musical instruments based on solving differential equations is of great interest in Musical Acoustics especially in terms of linking geometry features of musical instruments to sound features. A major restriction of accurate physical models is the computational effort. One could state that the calculation cost is directly linked to the geometrical and material accuracy of a physical model and so to the validity of the results. This work presents a methodology for implementing realtime models of whole instrument geometries modelled with the Finite Differences Method (FDM) on a Field Programmable Gate Array (FPGA), a device capable of massively parallel computations. Examples of three real-time musical instrument implementations are given, a Banjo, a Violin and a Chinese Ruan.

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In this paper we present a virtual analog model of the voltagecontrolled filter used in the EDP Wasp synthesizer. This circuit is an interesting case study for virtual analog modeling due to its characteristic nonlinear and highly dynamic behavior which can be attributed to its unusual design. The Wasp filter consists of a state variable filter topology implemented using operational transconductance amplifiers (OTAs) as the cutoff-control elements and CMOS inverters in lieu of operational amplifiers, all powered by a unipolar power supply. In order to accurately model the behavior of the circuit we propose extended models for its nonlinear components, focusing particularly on the OTAs. The proposed component models are used inside a white-box circuit modeling framework to create a digital simulation of the filter which retains the interesting characteristics of the original device.

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This paper proposes dark velvet noise (DVN) as an extension of the original velvet noise with a lowpass spectrum. The lowpass spectrum is achieved by allowing each pulse in the sparse sequence to have a randomized pulse width. The cutoff frequency is controlled by the density of the sequence. The modulated pulse-width can be implemented efficiently utilizing a discrete set of recursive running-sum filters, one for each unique pulse width. DVN may be used in reverberation algorithms. Typical room reverberation has a frequency-dependent decay, where the high frequencies decay faster than the low ones. A similar effect is achieved by lowering the density and increasing the pulse-width of DVN in time, thereby making the DVN suitable for artificial reverberation.

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Modal methods are a long-established approach to physical modeling sound synthesis. Projecting the equation of motion of a linear, time-invariant system onto a basis of eigenfunctions yields a set of independent forced, lossy oscillators, which may be simulated efficiently and accurately by means of standard time-stepping methods. Extensions of modal techniques to nonlinear problems are possible, though often requiring the solution of densely coupled nonlinear time-dependent equations. Here, an application of recent results in numerical simulation design is employed, in which the nonlinear energy is first quadratised via a convenient auxiliary variable. The resulting equations may be updated in time explicitly, thus avoiding the need for expensive iterative solvers, dense linear system solutions, or matrix inversions. The case of a network of interconnected distributed elements is detailed, along with a real-time implementation as an audio plugin.

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Acceleration of audio workloads on generally-programmable GPU (GPGPU) hardware offers potentially high speedup factors, but also presents challenges in terms of development and deployment. We can increasingly depend on such hardware being available in users’ systems, yet few real-time audio products use this resource. We propose a suite of benchmarks to qualify a GPU as suitable for batch or real-time audio processing. This includes both microbenchmarks and higher-level audio domain benchmarks. We choose metrics based on application, paying particularly close attention to latency tail distribution. We propose an extension to the benchmark framework to more accurately simulate the real-world request pattern and performance requirements when running in a digital audio workstation. We run these benchmarks on two common consumer-level platforms: a PC desktop with a recent midrange discrete GPU and a Macintosh desktop with unified CPUGPU memory architecture.

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The acoustic piano and its sound production mechanisms have been extensively studied in the field of acoustics. Similarly, digital piano synthesis has been the focus of numerous signal processing research studies. However, the role of the piano action in shaping the dynamics and nuances of piano sound has received less attention, particularly in the context of digital pianos. Digital pianos are well-established commercial instruments that typically use weighted keys with two or three sensors to measure the average key velocity—this being the only input to a sampling synthesis engine. In this study, we investigate whether this simplified measurement method adequately captures the full dynamic behavior of the original piano action. After a brief review of the state of the art, we describe an experimental setup designed to measure physical properties of the keys and hammers of a piano. This setup enables high-precision readings of acceleration, velocity, and position for both the key and hammer across various dynamic levels. Through extensive data analysis, we examine their relationships and identify the optimal key position for velocity measurement. We also analyze a digital piano key to determine where the average key velocity is measured and compare it with our proposed optimal timing. We find that the instantaneous key velocity just before let-off correlates most strongly with hammer impact velocity, indicating a target for improved sensing; however, due to the limitations of discrete velocity sensing this optimization alone may not suffice to replicate the nuanced expressiveness of acoustic piano touch. This study represents the first step in a broader research effort aimed at linking piano touch, dynamics, and sound production.

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This paper introduces a digital reconstruction of the morphophone, a complex magnetophonic device developed in the 1950s within the laboratories of the GRM (Groupe de Recherches Musicales) in Paris. The analysis, design, and implementation methodologies underlying the Digital Morphophone Environment are discussed. Based on a detailed review of historical sources and limited documentation – including a small body of literature and, most notably, archival images – the core operational principles of the morphophone have been modeled within the MAX visual programming environment. The main goals of this work are, on the one hand, to study and make accessible a now obsolete and unavailable tool, and on the other, to provide the opportunity for new explorations in computer music and research.

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