Deep clustering: Discriminative embeddings for segmentation and separation

We address the problem of acoustic source separation in a deep learning framework we call "deep clustering." Rather than directly estimating signals or masking functions, we train a deep network to produce spectrogram embeddings that are discriminative for partition labels given in training data. Previous deep network approaches provide great advantages in terms of learning power and speed, but previously it has been unclear how to use them to separate signals in a class-independent way. In contrast, spectral clustering approaches are flexible with respect to the classes and number of items to be segmented, but it has been unclear how to leverage the learning power and speed of deep networks. To obtain the best of both worlds, we use an objective function that to train embeddings that yield a low-rank approximation to an ideal pairwise affinity matrix, in a class-independent way. This avoids the high cost of spectral factorization and instead produces compact clusters that are amenable to simple clustering methods. The segmentations are therefore implicitly encoded in the embeddings, and can be "decoded" by clustering. Preliminary experiments show that the proposed method can separate speech: when trained on spectrogram features containing mixtures of two speakers, and tested on mixtures of a held-out set of speakers, it can infer masking functions that improve signal quality by around 6dB. We show that the model can generalize to three-speaker mixtures despite training only on two-speaker mixtures. The framework can be used without class labels, and therefore has the potential to be trained on a diverse set of sound types, and to generalize to novel sources. We hope that future work will lead to segmentation of arbitrary sounds, with extensions to microphone array methods as well as image segmentation and other domains.Comment: Originally submitted on June 5, 201

Using multiple frequency bins for stabilization of FD-ICA algorithms

In the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.Fil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin