A framework for real-time expressive modification of audio musical performances is presented. An expressiveness model compute the deviations of the musical parameters which are relevant in terms of control of the expressive intention. The modifications are then realized by the integration of the model with a sound processing engine.

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This paper presents the results of an experiment aimed to evaluate the quality of different audio restoration algorithms based on different Short Time Spectral Attenuation methods. To single out the best computational methodologies for audio restoration an experiment was made, implementing a software (in DirectX plug-in form) which uses different algorithms. The software, working in real time, permits to compare the different algorithms in a objective way: in fact, it is possible to use the same software environment to operate the restorations with different algorithms. In this way, is feasible to compare different methods. In the paper we will first shortly overview the most used audio restoration methods and, in particular, the algorithms implemented in our software. Then we will present a timefrequency analysis of the restored stimuli to show the main advantages and drawbacks of the different algorithms used.

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This paper proposes innovative de-noise filters in a framework, whose aim is the localization of an acoustic source in a noisy environment. The main focuses are the automatic detection of transient sound events and the separation of the events of interest from the noise. A microphone array is used to capture timespatial information and an adaptive filter can be initialized to learn the ambient noise spectrum when signals of interest are absent. We propose an algorithm based on the Short Time Spectral Attenuation method to remove the noise from each sensor of the array, before the source localization task is performed. The Time Difference Of Arrival (TDOA) methods are used for multiple sources localization. The experimental results show the efficiency of our framework in stationary noisy environments.

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The growing demand for automatic surveillance systems that integrates different types of sensors, including microphones, requires to adapt and optimize the already studied techniques of Acoustic Source Localization to meet the constraints imposed by the new application scenario. In this paper, we present a real-time prototype for multiple acoustic sources localization in a far-filed and free-field environment. The prototype is composed by two linear arrays and utilizes an innovative approach for the localization of multiple sources. The algorithm is based on two steps: i) the separation of the sources by means of beamforming techniques and ii) the comparison of the power spectrum by means of a spectral distance measure. The prototype was successfully tested in a real environment.

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In this paper, we focus on the signal-to-noise ratio (SNR) improvement in single channel audio recordings. Many approaches have been reported in the literature. The most popular method, with many variants, is Short Time Spectral Attenuation (STSA). Although this method reduces the noise and improves the SNR, it mostly tends to introduce signal distortion and a perceptually annoying residual noise usually called musical noise. In this paper we investigate the use of Non-negative Matrix Factorization (NMF) as an alternative to the STSA for the digital curation of musical heritage. NMF is an emerging new technique in the blind extraction of signals recorded in a variety of different fields. The application of NMF to the analysis of monaural recordings is relatively recent. We show that NMF is a suitable technique to extract the clean audio signal from undesired non stationary noise in a monaural recording of ethnic music. More specifically, we introduce a perceptual suppression rule to determine how the perceptual domain is competitive compared to the acoustic domain. Moreover, we carry out a listening test in order to compare NMF with the state of the art audio restoration framework using the EBU MUSHRA test method. The encouraging results obtained with this methodology in the presented case study support their wider applicability in audio separation.

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This paper describes the implementation of an innovative musical interface based on the sound localization capability of a microphone array. Our proposal is to allow a musician to plan and conduct the expressivity of a performance, by controlling in realtime an audio processing module through the spatial movement of a sound source, i.e. voice, traditional musical instruments, sounding mobile devices. The proposed interface is able to locate and track the sound in a two-dimensional space with accuracy, so that the x-y coordinates of the sound source can be used to control the processing parameters. In particular, the paper is focused on the localization and tracking of harmonic sound sources in real moderate reverberant and noisy environment. To this purpose, we designed a system based on adaptive parameterized Generalized Cross-Correlation (GCC) and Phase Transform (PHAT) weighting with Zero-Crossing Rate (ZCR) threshold, a Wiener filter to improve the Signal to Noise Ratio (SNR) and a Kalman filter to make the position estimation more robust and accurate. We developed a Max/MSP external objects to test the system in a real scenario and to validate its usability.

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