Download Additive Synthesis Of Sound By Taking Advantage Of Psychoacoustics In this paper we present an original technique designed in order to speed up additive synthesis. This technique consists in taking into account psychoacoustic phenomena (thresholds of hearing and masking) in order to ignore the inaudible partials during the synthesis process, thus saving a lot of computation time. Our algorithm relies on a specific data structure called “skip list” and has proven to be very efficient in practice. As a consequence, we are now able to synthesize an impressive number of spectral sounds in real time, without overloading the processor.
Download Sinusoidal Parameter Extraction and Component Selection in a non Stationary Model In this paper, we introduce a new analysis technique particularly suitable for the sinusoidal modeling of non-stationary signals. This method, based on amplitude and frequency modulation estimation, aims at improving traditional Fourier parameters and enables us to introduce a new peak selection process, so that only peaks having coherent parameters are considered in subsequent stages (e.g. partial tracking, synthesis). This allows our spectral model to better handle natural sounds.
Download Enhanced partial tracking using linear prediction In this paper, we introduce a new partial tracking method suitable for the sinusoidal modeling of mixtures of instrumental sounds with pseudo-stationary frequencies. This method, based on the linear prediction of the frequency evolutions of the partials, enables us to track these partials more accurately at the analysis stage, even in complex sound mixtures. This allows our spectral model to better handle polyphonic sounds.
Download Improving Sinusoidal Frequency Estimation Using a Trigonometric Approach Estimating the frequency of sinusoidal components is the first part of the sinusoidal analysis chain. Among numerous frequency estimators presented in the literature, we propose to study an estimator proposed in [1] known as the derivative algorithm. Thanks to a trigonometric interpretation of this frequency estimator, we are able to propose a new estimator which improves estimation performance for the frequencies close to the Nyquist frequency without any computational overload.
Download Assessing the Quality of the Extraction and Tracking of Sinusoidal Components: Towards an Evaluation Methodology In this paper, we introduce two original evaluation methods in the context of sinusoidal modeling. The first one assesses the quality of the extraction of sinusoidal components from short-time signals, whereas the second one focuses on the quality of the tracking of these sinusoidal components over time. Each proposed method intends to use a unique cost function that globally reflects the performance of the tested algorithm in a realistic framework. Clearly defined evaluation protocols are then proposed with several test cases to evaluate most of the desired properties of extractors or trackers of sinusoidal components. This paper is a first proposal to be used as a starting point in a sinusoidal analysis / synthesis contest to be held at DAFx’07.
Download Adaptive Harmonization and Pitch Correction of Polyphonic Audio Using Spectral Clustering There are several well known harmonization and pitch correction techniques that can be applied to monophonic sound sources. They are based on automatic pitch detection and frequency shifting without time stretching. In many applications it is desired to apply such effects on the dominant melodic instrument of a polyphonic audio mixture. However, applying them directly to the mixture results in artifacts, and automatic pitch detection becomes unreliable. In this paper we describe how a dominant melody separation method based on spectral clustering of sinusoidal peaks can be used for adaptive harmonization and pitch correction in mono polyphonic audio mixtures. Motivating examples from a violin tutoring perspective as well as modifying the saxophone melody of an old jazz mono recording are presented.
Download On the control of the phase of resonant filters with applications to percussive sound modeling Source-filter models are widely used in numerous audio processing fields, from speech processing to percussive/contact sound synthesis. The design of filters for these models—be it from scratch or from spectral analysis—usually involves tuning frequency and damping parameters and/or providing an all-pole model of the resonant part of the filter. In this context, and for the modelling of percussive (non-sustained) sounds, a source signal can be estimated from a filtered sound through a time-domain deconvolution process. The result can be plagued with artifacts when resonances exhibit very low bandwidth and lie very close in frequency. We propose in this paper a method that noticeably reduces the artifacts of the deconvolution process through an inter-resonance phase synchronization. Results show that the proposed method is able to design filters inducing fewer artifacts at the expense of a higher dynamic range.
Download Two-step modal identification for increased resolution analysis of percussive sounds Modal synthesis is a practical and efficient way to model sounding structures with strong resonances. In order to create realistic sounds, one has to be able to extract the parameters of this model from recorded sounds produced by the physical system of interest. Many methods are available to achieve this goal, and most of them require a careful parametrization and a post-selection of the modes to guarantee a good quality/complexity trade-off. This paper introduces a two step analysis method aiming at an automatic and reliable identification of the modes. The first step is performed at a global level with few assumptions about the spectro/temporal content of the considered signal. From the knowledge gained with this global analysis, one can focus on specific frequency regions and perform a local analysis with strong assumptions. The gains of such a two step approach are a better estimation of the number of modal components as well as a better estimate of their parameters.
Download The DESAM Toolbox: Spectral Analysis of Musical Audio 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.
Download Characterisation of Acoustic Scenes Using a Temporally-constrained Shift-invariant Model In this paper, we propose a method for modeling and classifying acoustic scenes using temporally-constrained shift-invariant probabilistic latent component analysis (SIPLCA). SIPLCA can be used for extracting time-frequency patches from spectrograms in an unsupervised manner. Component-wise hidden Markov models are incorporated to the SIPLCA formulation for enforcing temporal constraints on the activation of each acoustic component. The time-frequency patches are converted to cepstral coefficients in order to provide a compact representation of acoustic events within a scene. Experiments are made using a corpus of train station recordings, classified into 6 scene classes. Results show that the proposed model is able to model salient events within a scene and outperforms the non-negative matrix factorization algorithm for the same task. In addition, it is demonstrated that the use of temporal constraints can lead to improved performance.