In this paper we investigate the problem of singer identification on acapella recordings of isolated notes. Most of studies on singer identification describe the content of signals of singing voice with features related to the timbre (such as MFCC or LPC). These features aim to describe the behavior of frequencies at a given instant of time (local features). In this paper, we propose to describe sung tone with the temporal variations of the fundamental frequency (and its harmonics) of the note. The periodic and continuous variations of the frequency trajectories are analyzed on the whole note and the features obtained reflect expressive and intonative elements of singing such as vibrato, tremolo and portamento. The experiments, conducted on two distinct data-sets (lyric and pop-rock singers), prove that the new set of features capture a part of the singer identity. However, these features are less accurate than timbre-based features. We propose to increase the recognition rate of singer identification by combining information conveyed by local and global description of notes. The proposed method, that shows good results, can be adapted for classification problem involving a large number of classes, or to combine classifications with different levels of performance.

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IRCAM has a long experience in analysis, synthesis and transformation of voice. Natural voice transformations are of great interest for many applications and can be combine with text-to-speech system, leading to a powerful creation tool. We present research conducted at IRCAM on voice transformations for the last few years. Transformations can be achieved in a global way by modifying pitch, spectral envelope, durations etc. While it sacrifices the possibility to attain a specific target voice, the approach allows the production of new voices of a high degree of naturalness with different gender and age, modified vocal quality, or another speech style. These transformations can be applied in realtime using ircamTools TR A X.Transformation can also be done in a more specific way in order to transform a voice towards the voice of a target speaker. Finally, we present some recent research on the transformation of expressivity.

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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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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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