This paper presents non-negative factorization of audio signals for the binaural localization of multiple sound sources within realistic and unknown sound environments. Non-negative tensor factorization (NTF) provides a sparse representation of multichannel audio signals in time, frequency, and space that can be exploited in computational audio scene analysis and robot audition for the separation and localization of sound sources. In the proposed formulation, each sound source is represented by means of spectral dictionaries, temporal activation, and its distribution within each channel (here, left and right ears). This distribution, being dependent on the frequency, can be interpreted as an explicit estimation of the Head-Related Transfer Function (HRTF) of a binaural head which can then be converted into the estimated sound source position. Moreover, the semisupervised formulation of the non-negative factorization allows us to integrate prior knowledge about some sound sources of interest whose dictionaries can be learned in advance, whereas the remaining sources are considered as background sound, which remains unknown and is estimated on the fly. The proposed NTF-based sound source localization is applied here to binaural sound source localization of multiple speakers within realistic sound environments.

This paper introduces a constrained source/filter model for semi-supervised speech separation based on non-negative matrix factorization (NMF). The objective is to inform NMF with prior knowledge about speech, providing a physically meaningful speech separation. To do so, a source/filter model (indicated as Instantaneous Mixture Model or IMM) is integrated in the NMF. Furthermore, constraints are added to the IMM-NMF, in order to control the NMF behaviour during separation, and to enforce its physical meaning. In particular, a speech specific constraint - based on the source/filter coherence of speech - and a method for the automatic adaptation of constraints' weights during separation are presented. Also, the proposed source/filter model is semi-supervised: during training, one filter basis is estimated for each phoneme of a speaker; during separation, the estimated filter bases are then used in the constrained source/filter model. An experimental evaluation for speech separation was conducted on the TIMIT speakers database mixed with various environmental background noises from the QUT-NOISE database. This evaluation showed that the use of adaptive constraints increases the performance of the source/filter model for speaker-dependent speech separation, and compares favorably to fully-supervised speech separation.

This paper presents an investigation into the detection and classification of drum sounds in polyphonic music and drum loops using non-negative matrix deconvolution (NMD) and the Itakura Saito divergence. The Itakura Saito divergence has recently been proposed as especially appropriate for decomposing audio spectra due to the fact that it is scale invariant, but it has not yet been widely adopted. The article studies new contributions for audio event detection methods using the Itakura Saito divergence that improve efficiency and numerical stability, and simplify the generation of target pattern sets. A new approach for handling background sounds is proposed and moreover, a new detection criteria based on estimating the perceptual presence of the target class sources is introduced. Experimental results obtained for drum detection in polyphonic music and drum soli demonstrate the beneficial effects of the proposed extensions.

The following article describes research on source detection in multi channel (3DTV) audio streams. The problem is extremely complex due to the fact that multiple layers can be present in scenes (background music, ambience, commentator). In this work a new algorithm is developed that exploits the information from the different audio channels to detect, and possibly localize and separate independent audio sources. An algorithm based on online Non-negative Tensor Deconvolution is realized, to deal with sound sources with time dependent positions in the channel matrix. The evaluation is made on 3DTV 5.1 film soundtracks and on synthetic mixes of 3DTV 5.1 audio with target sounds from a sound effects database: a significant improvement of the detection performance is shown, compared with other decomposition techniques.

In this paper, we propose a way of using the Audio-Visual Description Profile (AVDP) of the MPEG-7 standard for 2D or stereo video and multichannel audio content description. Our aim is to provide means of using AVDP in such a way, that 3D video and audio content can be correctly and consistently described. Since AVDP semantics do not include ways for dealing with 3D audiovisual content, a new semantic framework within AVDP is proposed and examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content are presented.

We present an algorithm for sound analysis and re-synthesis with local automatic adaptation of time-frequency resolution. The reconstruction formula we propose is highly efficient, and gives a good approximation of the original signal from analyses with different time-varying resolutions within complementary frequency bands: this is a typical case where perfect reconstruction cannot in general be achieved with fast algorithms, which provides an error to be minimized. We provide a theoretical upper bound for the reconstruction error of our method, and an example of automatic adaptive analysis and re-synthesis of a music sound.

This paper introduces an entropy-based spectral representation as a measure of the degree of noisiness in audio signals, complementary to the standard MFCCs for audio and speech recognition. The proposed representation is based on the Rényi entropy, which is a generalization of the Shannon entropy. In audio signal representation, Rényi entropy presents the advantage of focusing either on the harmonic content (prominent amplitude within a distribution) or on the noise content (equal distribution of amplitudes). The proposed representation outperforms all other noisiness measures - including Shannon and Wiener entropies - in a large-scale classification of vocal effort (whispered-soft/normal/loud-shouted) in the real scenario of multi-language massive role-playing video games. The improvement is around 10% in relative error reduction, and is particularly significant for the recognition of noisy speech - i.e., whispery/breathy speech. This confirms the role of noisiness for speech recognition, and will further be extended to the classification of voice quality for the design of an automatic voice casting system in video games.

Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time-frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the Rényi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.

Iannis Xenakis' works Duel and Strategie are two music games: sounds play the role of moves in a match where the players are the two orchestra conductors. They decide which part of the score is to be played in answer to the opposite conductor's choice, looking at a game matrix which contains the values of every couple of moves.Playing Music is an installation driven by software implementing the same logic as Duel.Each player makes his moves with simple physical actions, recognized by the software using a camera, and the score is projected on a screen so that the audience can easily understand the rules, after a few moves.