In the field of musicians and recording engineers audio effects are mainly described and indicated by their acoustical effect. Audio effects can also be categorized from a technical point of view. The main criterion is found to be the type of modulation technique used to achieve the effect. After a short introduction to the different modulation types, three more sophisticated audio effect applications are presented, namely single sideband domain vibrato (mechanical vibrato bar simulation), a rotary speaker simulation, and an enhanced pitch transposing scheme.

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In previous work, a class of digital sound synthesis methods was introduced using iterated nonlinear functions [1][2][3][4]. Within the phase space of any method in the class, we encounter regions of special interest where signals have peculiar self-similar structures (waveforms of multiple fractal contours) [5][6]. Due to the system dynamics, emergent properties in the output sound signal result into acoustic turbulences and other textural sound phenomena. Parallel work was pursued, both in computer music research [7] and in the auditory display of experimental data, using chaotic oscillators [8] (nonlinear pendulums are also illustrated in [9]). This paper discusses the use of iterated nonlinear functions in the modelling of the perceptual attributes in complex auditory images. Based on the chaotic dynamics in such algorithms, it is possible to create textural and environmental sound effects of a peculiar kind, hardly obtained with other methods. Examples include sound textures reminiscent of rains, thunderstorms and more articulated phenomena of acoustic turbulence. This research opens to new experiments in electroacoustic music and the creation of synthetic, but credible, auditory scenes in multimedia applications and virtual reality.

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This paper deals with designing material parameters for physical models. It is shown that the characteristic relation between modal frequencies and damping factors of a sound object is the acoustic invariant of the material from which the body is made. Thus, such characteristic relation can be used for designing damping models for a conservative physical model to represent a particular material.

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The new MSP extension to the Max programming environment provides an easily comprehensible and versatile way to program realtime DSP applications. Because of its full integration into Max, MSP allows one to combine MIDI data and audio data readily in any program, and to hear the results immediately. This makes it an excellent environment for experimenting with new DSP algorithms and for designing music performances with a realtime DSP component. This paper presents some algorithms for time-domain audio processing in MSP which are not commonly found in the repertoire of included effects for commercially available audio processors. These algorithms—which use the realtime segmentation of captured audio—are computationally inexpensive, yet are capable of producing a variety of interesting sonic effects. They include simulated time-compression and pitch-shifting of audio samples, segmentation of audio samples for use as “notes” in another rhythmic structure, and modulation to extreme rates of sample playback.

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In many high-level signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.

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Dispersive tapped delay lines are attractive structures for altering the frequency content of a signal. In previous papers we showed that in the case of a homogeneous line with first order all-pass sections the signal formed by the output samples of the chain of delays at a given time is equivalent to compute the Laguerre transform of the input signal. However, most musical signals require a time-varying frequency modification in order to be properly processed. Vibrato in musical instruments or voice intonation in the case of vocal sounds may be modeled as small and slow pitch variations. Simulations of these effects require techniques for time- varying pitch and/or brightness modification that are very useful for sound processing. In our experiments the basis for time-varying frequency warping is a time-varying version of the Laguerre transformation. The corre- sponding implementation structure is obtained as a dispersive tapped delay line, where each of the frequency dependent delay element has its own phase response. Thus, time-varying warping results in a space-varying, inhomogeneous, propagation structure. We show that time-varying frequency warping may be associated to expansion over biorthogonal sets generalizing the discrete Laguerre basis. Slow time-varying characteristics lead to slowly varying parameter sequences. The corresponding sound transformation does not suffer from discontinuities typical of delay lines based on unit delays.

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A new architecture for musical distortion is proposed. Based on WaveShaping as the distortion generation element, a multiband front-end is used in order to extract simple (ideally monotonal) non-full band signals. Distortion pattern can be adjusted per band, with the benefit that intermodulation distortion is kept low, balancing the end result towards ‘harmonic’ distortion rather than ‘metallic’ or ‘ringing’. Many parameters can be adjusted by the end user in a musically meaningful domain, allowing the creation of rich, detailed and highly personal sounding distortions to be imposed over real world signals.

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Physical modelling of musical instruments is one possible approach to digital sound synthesis techniques. By the term physical modelling, we refer to the simulation of sound production mechanism of a musical instrument, which is modelled with reference to the physics using wave-guides. One of the fundamental parameters of such a physical model is the pitch, and so pitch period estimation is one of the first tasks of any analysis of such a model. In this paper, an algorithm based on the Dyadic Wavelet Transform has been investigated for pitch detection of musical signals. The wavelet transform is simply the convolution of a signal f(t) with a dialated and translated version of a single function called the mother wavelet that has to satisfy certain requirements. There are a wide variety of possible wavelets, but not all are appropriate for pitch detection. The performance of both linear phase wavelets (Haar, Morlet, and the spline wavelet) and minimum phase wavelets (Daubechies’ wavelets) have been investigated. The algorithm proposed here has proved to be simple, accurate, and robust to noise; it also has the potential of acceptable speed. A comparative study between this algorithm and the well-known autocorrelation function is also given. Finally, illustrative examples of different real guitar tones and other sound signals are given using the proposed algorithm. KEYWORDS Physical modeling – wavelet transform – pitch – autocorrelation function.

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The separation of musical instruments acoustically mixed in one source is a very active field which has been approached from many different viewpoints. This article compares the blind source separation perspective and oscillatory correlation theory taking the auditory scene analysis as the point of departure (ASA). The former technique deals with the separation of a particular signal from a mixture with many others from a statistical point of view. Through the standard Independent Component Analysis (ICA), a blind source separation can be done using the particular and the mixed signals' statistical properties. Thus, the technique is general and does not use previous knowledge about musical instruments. In the second approach, an ASA extension is studied with a dynamic neural model which is able to separate the different musical instruments taking a priori unknown perceptual elements as a point of departure. Applying an inverse transformation to the output of the model, the different contributions to the mixture can be recovered again in the time domain.

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This paper is concerned with application of Number Theoretic Transforms (NTTs) to audio processing. The problem of dynamic range is of particular interest for this application and is therefore treated in some detail.

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