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In Cajun slang, Lagniappe means something extra, and ICASSP 2017 is really a Lagniappe. On behalf of ICASSP 2017 and IEEE Signal Processing Society, I welcome you to the beautiful and historic city of New Orleans. New Orleans is the heart of great bayous; the melting pot of Cajun, Zydeco, and Creole cultures; the capital of Jazz music; and the home of Mardi Gras.
Welcome to ICASSP 2017, the world's largest and most comprehensive conference on Acoustics, Speech, and Signal Processing, held this year in the beautiful city of New Orleans, Louisiana—the home of jazz, year long festivities, and a unique cuisine with a Cajun kick. We are happy to welcome you to New Orleans and hope you will enjoy the colors of the city, the music, the active nightlife, and of course...
We propose a novel informed source separation method for audio object coding based on a recent sampling theory for smooth signals on graphs. Assuming that only one source is active at each time-frequency point, we compute an ideal map indicating which source is active at each time-frequency point at the encoder. This map is then sampled with a compressive graph signal sampling strategy that guarantees...
In this paper we tackle the problem of single channel audio source separation driven by descriptors of the sounding object's motion. As opposed to previous approaches, motion is included as a soft-coupling constraint within the nonnegative matrix factorization framework. The proposed method is applied to a multimodal dataset of instruments in string quartet performance recordings where bow motion...
In this paper, we propose a new supervised monaural source separation based on autoencoders. We employ the autoencoder for the dictionary training such that the nonlinear network can encode the target source with high expressiveness. The dictionary is trained by each target source without the mixture signal, which makes the system independent from the context where the dictionaries will be used. In...
We present a probabilistic model for joint source separation and diarisation of multichannel convolutive speech mixtures. We build upon the framework of local Gaussian model (LGM) with non-negative matrix factorization (NMF). The diarisation is introduced as a temporal labeling of each source in the mix as active or inactive at the short-term frame level. We devise an EM algorithm in which the source...
In this paper, we propose a new blind source separation (BSS) method based on independent low-rank matrix analysis (ILRMA) with novel sparse regularization. ILRMA is a recently proposed BSS algorithm that simultaneously estimates a demixing matrix and source spectrogram models based on nonnegative matrix factorization (NMF). To improve the separation accuracy and stability, an additional constraint...
A great number of methods for multichannel audio source separation are based on probabilistic approaches in which the sources are modeled as latent random variables in a Time-Frequency (TF) domain. For reverberant mixtures, it is common to approximate the time-domain convolutive mixing process as being instantaneous in the short-term Fourier transform domain, under a short mixing filters assumption...
In this paper we propose a supervised Nonnegative Matrix Factorization (NMF) model for overlapping sound event detection in real life audio. We start by highlighting the usefulness of non-euclidean NMF to learn representations for detecting and classifying acoustic events in a multi-label setting. Then, we propose to learn a classifier and the NMF decomposition in a joint optimization problem. This...
This paper presents supervised feature learning approaches for speaker identification that rely on nonnegative matrix factorisation. Recent studies have shown that group nonnegative matrix factorisation and task-driven supervised dictionary learning can help performing effective feature learning for audio classification problems. This paper proposes to integrate a recent method that relies on group...
The estimation of rhythmic properties such as tempo, beat positions or metrical structure are central aspects of Music Information Retrieval (MIR) research. Meter inference algorithms are typically designed to track metrical structure in presence of mild deviations of the feature estimates over time in order to account for performance imprecisions, expressive timing or musical effects such as accelerando...
In this paper, we propose a supervised multilayer factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative matrix factorization which is able to exploit time-frequency dictionaries to take into...
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