Identifying the acoustical modes of a resonant object can be achieved by expanding a recorded impact sound in a sum of damped sinusoids. High-resolution methods, e.g. the ESPRIT algorithm, can be used, but the time-length of the signal often requires a sub-band decomposition. This ensures, thanks to sub-sampling, that the signal is analysed over a significant duration so that the damping coefficient of each mode is estimated properly, and that no frequency band is neglected. In this article, we show that the ESPRIT algorithm can be efficiently applied in a Gabor transform (similar to a sub-sampled short-time Fourier transform). The combined use of a time-frequency transform and a high-resolution analysis allows selective and sharp analysis over selected areas of the time-frequency plane. Finally, we show that this method produces high-quality resynthesized impact sounds which are perceptually very close to the original sounds.

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A parametric model of aural tuning of acoustic pianos is presented in this paper. From a few parameters, a whole tessitura model is obtained, that can be applied to any kind of pianos. Because the tuning of piano is strongly linked to the inharmonicity of its strings, a 2-parameter model for the inharmonicity coefficient along the keyboard is introduced. Constrained by piano string design considerations, its estimation requires only a few notes in the bass range. Then, from tuning rules, we propose a 4-parameter model for the fundamental frequency evolution on the whole tessitura, taking into account the model of the inhamonicity coefficient. The global model is applied to 5 different pianos (4 grand pianos and

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In the context of audio restoration, sound transfer of broken disks usually produces audio signals corrupted with long pulses of low-frequency content, also called thumps. This paper presents a method for audio de-thumping based on Huang’s Empirical Mode Decomposition (EMD), provided the pulse locations are known beforehand. Thus, the EMD is used as a means to obtain pulse estimates to be subtracted from the degraded signals. Despite its simplicity, the method is demonstrated to tackle well the challenging problem of superimposed pulses. Performance assessment against selected competing solutions reveals that the proposed solution tends to produce superior de-thumping results.

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The use of high quality sound effects is growing rapidly in multimedia, interactive and virtual reality applications. The common source of audio events in these applications is impact sounds. The sound effects in such environments can be pre-recorded or synthesized in real-time as a result of a physical event. However, one of the biggest problems when using pre-recorded sound effects is the monotonous repetition of these sounds which can be tedious to the listener. In this paper, we present a new algorithm which generates non-repetitive impact sound effects using parameters from the physical interaction. Our approach aims to use audio grains to create finely-controlled synthesized sounds which are based on recordings of impact sounds. The proposed algorithm can also be used in a large set of audio data analysis, representation, and compression applications. A subjective test was carried out to evaluate the perceptual quality of the synthesized sounds.

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In ensemble music performance, such as a string quartet or duet, the musicians interact and influence each other’s performance via a multitude of parameters – including tempo, dynamics, articulation of musical phrases and, depending on the type of instrument, intonation. This paper presents our ongoing research on the effect of interaction between violinists, in terms of intonation. We base our analysis on a series of experiments with professional as well as amateur musicians playing in duet and solo experimental set-ups, and then apply a series of interdependence measures on each violinist’s pitch deviations from the score. Our results show that while it is possible to, solely based on intonation, distinguish between solo and duet performances for simple cases, there is a multitude of underlying factors that need to be analyzed before these techniques can be applied to more complex pieces and/or non-experimental situations.

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Timbral modeling is fundamental in content based music similarity systems. It is usually achieved by modeling the short term features by a Gaussian Model (GM) or Gaussian Mixture Models (GMM). In this article we propose to achieve this goal by using the GMM-supervector approach. This method allows to represent complex statistical models by an Euclidean vector. Experiments performed for the music similarity task showed that this model outperform state of the art approches. Moreover, it reduces the similarity search time by a factor of ≈ 100 compared to state of the art GM modeling. Furthermore, we propose a new supervector normalization which makes the GMM-supervector approach more preformant for the music similarity task. The proposed normalization can be applied to other Euclidean models.

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We propose here an original method for the automatic alignment of temporally distorted occurrences of audio items. The method is based on a so-called item-restricted fingerprinting process and a segment detection scheme. The high-precision estimation of the temporal distortions allows to compensate these alterations and obtain a perfect synchronization between the original item and the altered occurrence. Among the applications of this process, we focus on the verification and the alignment of audio fingerprinting annotations. Perceptual evaluation confirms the efficiency of the method in detecting wrong annotations, and confirms the high precision of the synchronization on the occurrences.

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The use of mid-level audio features has recently shown promising perspectives for music content description tasks. In this paper we investigate the use of a mid-level harmony-related descriptor for the task of music structure analysis. The idea behind the descriptor is to concatenate the chroma sequence of a musical segment in a Multi Probe Histogram (MPH). Probing local pitch intervals within the chroma sequence, we show that such an abstraction of the audio signal is a good descriptor of the tonal hierarchy that is consequent to a given key or melodic line. It is thus a powerful feature for describing harmonic and melodic progressions within a music piece. After introducing the histograms’ computation and enlightening their relation to harmony, we use such a description for the task of music structure segmentation and provide preliminary results that show very encouraging perspectives.

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In this paper we address the problem of the description of music production techniques from the audio signal. Over the past decades sound engineering techniques have changed drastically. New recording technologies, extensive use of compressors and limiters or new stereo techniques have deeply modified the sound of records. We propose three features to describe these evolutions in music production. They are based on the dynamic range of the signal, energy difference between channels and phase spread between channels. We measure the relevance of these features on a task of automatic classification of Pop/Rock songs into decades. In the context of Music Information Retrieval this kind of description could be very useful to better describe the content of a song or to assess the similarity between songs.

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