In this paper, a physics-based model for a snare drum will be discussed, along with its finite difference simulation. The interactions between a mallet and the membrane and between the snares and the membrane will be described as perfectly elastic collisions. A novel numerical scheme for the implementation of collisions will be presented, which allows a complete energy analysis for the whole system. Viscothermal losses will be added to the equation for the 3D wave propagation. Results from simulations and sound examples will be presented.

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In this study, a famous boxed effect pedal, also called stompbox, for electrical guitars is analyzed and simulated. The nodal DK method is used to create a non-linear state-space system with Matlab as a physical model for the MXR Phase 90 guitar effect pedal. A crucial component of the effect are Junction Field Effect Transistors (JFETs) which are used as variable resistors to dynamically vary the phase-shift characteristic of an allpass-filter cascade. So far, virtual analog modeling in the context of audio has mainly been applied to diode-clippers and vacuum tube circuits. This work shows an efficient way of describing the nonlinear behavior of JFETs, which are wide-spread in audio devices. To demonstrate the applicability of the proposed physical model, a real-time VST audio plug-in was implemented.

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We present an analysis of the bass drum circuit from the classic Roland TR-808 Rhythm Composer, based on physical models of the device’s many sub-circuits. A digital model based on this analysis (implemented in Cycling 74’s Gen˜) retains the salient features of the original and allows accurate emulation of circuit-bent modifications—complicated behavior that is impossible to capture through black-box modeling or structured sampling. Additionally, this analysis will clear up common misconceptions about the circuit, support the design of further drum machine modifications, and form a foundation for circuit-based musicological inquiry into the history of analog drum machines.

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In this paper, we propose the Modulation Scale Spectrum as an extension of the Modulation Spectrum through the Scale domain. The Modulation Spectrum expresses the evolution over time of the amplitude content of various frequency bands by a second Fourier Transform. While its use has been proven for many applications, it is not scale-invariant. Because of this, we propose the use of the Scale Transform instead of the second Fourier Transform. The Scale Transform is a special case of the Mellin Transform. Among its properties is "scale-invariance". This implies that two timestretched version of a same music track will have (almost) the same Scale Spectrum. Our proposed Modulation Scale Spectrum therefore inherits from this property while describing frequency content evolution over time. We then propose a specific implementation of the Modulation Scale Spectrum in order to represent rhythm content. This representation is therefore tempo-independent. We evaluate the ability of this representation to catch rhythm characteristics on a classification task. We demonstrate that for this task our proposed representation largely exceeds results obtained so far while being highly tempo-independent.

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We propose a new audio fingerprinting method that adapts findings from the field of blind astrometry to define simple, efficiently representable characteristic feature combinations called quads. Based on these, an audio identification algorithm is described that is robust to large amounts of noise and speed, tempo and pitch-shifting distortions. In addition to reliably identifying audio queries that are modified in this way, it also accurately estimates the scaling factors of the applied time/frequency distortions. We experimentally evaluate the performance of the method for a diverse set of noise, speed and tempo modifications, and identify a number of advantages of the new method over a recently published distortioninvariant audio copy detection algorithm.

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In this paper, we propose a novel algorithm for score-informed tracking of the fundamental frequency over the duration of single tones. The tracking algorithm is based on a peak-picking algorithm over spectral magnitudes and ensures time-continuous f0 -curves. From a set of 19 jazz solos from three saxophone and three trumpet players, we collected a set of 6785 f0 -contours in total. We report the results of two exploratory analyses. First, we compared typical contour feature values among different jazz musicians and different instruments. Second, we analyzed correlations between contour features and contextual parameters that describe the metrical position, the in-phrase position, and additional properties of each tone in a solo.

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This paper proposes a real-time capable method for transcribing and separating occurrences of single drum instruments in polyphonic drum recordings. Both the detection and the decomposition are based on Non-Negative Matrix Factorization and can be implemented with very small systemic delay. We propose a simple modification to the update rules that allows to capture timedynamic spectral characteristics of the involved drum sounds. The method can be applied in music production and music education software. Performance results with respect to drum transcription are presented and discussed. The evaluation data-set consisting of annotated drum recordings is published for use in further studies in the field. Index Terms - drum transcription, source separation, nonnegative matrix factorization, spectral processing, audio plug-in, music production, music education

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In this paper, a novel approach for the computation of a pitch salience function is presented. The aim of a pitch (considered here as synonym for fundamental frequency) salience function is to estimate the relevance of the most salient musical pitches that are present in a certain audio excerpt. Such a function is used in numerous Music Information Retrieval (MIR) tasks such as pitch, multiple-pitch estimation, melody extraction and audio features computation (such as chroma or Pitch Class Profiles). In order to compute the salience of a pitch candidate f , the classical approach uses the weighted sum of the energy of the short time spectrum at its integer multiples frequencies hf . In the present work, we propose a different approach which does not rely on energy but only on frequency location. For this, we first estimate the peaks of the short time spectrum. From the frequency location of these peaks, we evaluate the likelihood that each peak is an harmonic of a given fundamental frequency. The specificity of our method is to use as likelihood the deviation of the harmonic frequency locations from the pitch locations of the equal tempered scale. This is used to create a theoretical sequence of deviations which is then compared to an observed one. The proposed method is then evaluated for a task of multiple-pitch estimation using the MAPS test-set.

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Recently, we have witnessed an increasing use of the sourcefilter model in music analysis, which is achieved by integrating the source filter model into a non-negative matrix factorisation (NMF) framework or statistical models. The combination of the source-filter model and NMF framework reduces the number of free parameters needed and makes the model more flexible to extend. This paper compares four extended source-filter models: the source-filter-decay (SFD) model, the NMF with timefrequency activations (NMF-ARMA) model, the multi-excitation (ME) model and the source-filter model based on β-divergence (SFbeta model). The first two models represent the time-varying spectra by adding a loss filter and a time-varying filter, respectively. The latter two are extended by using multiple excitations and including a scale factor, respectively. The models are tested using sounds of 15 instruments from the RWC Music Database. Performance is evaluated based on the relative reconstruction error. The results show that the NMF-ARMA model outperforms other models, but uses the largest set of parameters.

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Exponentially damped sinusoids (EDS) model-based analysis of sound signals often requires a precise estimation of initial amplitudes and phases of the components found in the sound, on top of a good estimation of their frequencies and damping. This can be of the utmost importance in many applications such as high-quality re-synthesis or identification of structural properties of sound generators (e.g. a physical coupling of vibrating devices). Therefore, in those specific applications, an accurate estimation of the onset time is required. In this paper we present a two-step onset time estimation procedure designed for that purpose. It consists of a “rough" estimation using an STFT-based method followed by a time-domain method to “refine" the previous results. Tests carried out on synthetic signals show that it is possible to estimate onset times with errors as small as 0.2ms. These tests also confirm that operating first in the frequency domain and then in the time domain allows to reach a better resolution vs. speed compromise than using only one frequency-based or one time-based onset detection method. Finally, experiments on real sounds (plucked strings and actual percussions) illustrate how well this method performs in more realistic situations.

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