The automatic segmentation of isolated musical instrument sounds according to the temporal evolution is not a trivial task. It requires a model capable of capturing regions such as the attack, decay, sustain and release accurately for many types of instruments with different modes of excitation. The traditional ADSR amplitude envelope model does not apply universally to acoustic musical instrument sounds with different excitation methods because it uses strictly amplitude information and supposes all sounds manifest the same temporal evolution. We present an automatic segmentation technique based on a more realistic model of the temporal evolution of many types of acoustic musical instruments that incorporates both temporal and spectrotemporal cues. The method allows a robust and more perceptually relevant automatic segmentation of the isolated sounds of many musical instruments that fit the model.

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The aim of sound morphing is to obtain a sound that falls perceptually between two (or more) sounds. Ideally, we want to morph perceptually relevant features of sounds and be able to independently manipulate them. In this work we present a method to obtain perceptually intermediate spectral envelopes guided by highlevel spectral shape descriptors and a technique that employs evolutionary computation to independently manipulate the timbral features captured by the descriptors. High-level descriptors are measures of the acoustic correlates of salient timbre dimensions derived from perceptual studies, such that the manipulation of the descriptors corresponds to potentially interesting timbral variations.

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Nonstationary oscillations are ubiquitous in music and speech, ranging from the fast transients in the attack of musical instruments and consonants to amplitude and frequency modulations in expressive variations present in vibrato and prosodic contours. Modeling nonstationary oscillations with sinusoids remains one of the most challenging problems in signal processing because the fit also depends on the nature of the underlying sinusoidal model. For example, frequency modulated sinusoids are more appropriate to model vibrato than fast transitions. In this paper, we propose to model nonstationary oscillations with adaptive sinusoids from the extended adaptive quasi-harmonic model (eaQHM). We generated synthetic nonstationary sinusoids with different amplitude and frequency modulations and compared the modeling performance of adaptive sinusoids estimated with eaQHM, exponentially damped sinusoids estimated with ESPRIT, and log-linear-amplitude quadratic-phase sinusoids estimated with frequency reassignment. The adaptive sinusoids from eaQHM outperformed frequency reassignment for all nonstationary sinusoids tested and presented performance comparable to exponentially damped sinusoids.

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Sinusoids are widely used to represent the oscillatory modes of music and speech. The estimation of the sinusoidal parameters directly affects the quality of the representation. A parabolic interpolation of the peaks of the log-magnitude spectrum is commonly used to get a more accurate estimation of the frequencies and the amplitudes of the sinusoids at a relatively low computational cost. Recently, Werner and Germain proposed an improved sinusoidal estimator that performs parabolic interpolation of the peaks of a power-scaled magnitude spectrum. For each analysis window type and size, a power-scaling factor p is pre-calculated via a computationally demanding heuristic. Consequently, the powerscaling estimation method is currently constrained to a few tabulated power-scaling factors for pre-selected window sizes, limiting its practical applications. In this article, we propose a method to obtain the power-scaling factor p for any window size from the tabulated values. Additionally, we investigate the impact of zeropadding on the estimation accuracy of the power-scaled sinusoidal parameter estimator.

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