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

Deep Clustering and Conventional Networks for Music Separation: Stronger Together

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as music source separation. Contrary to conventional networks that directly estimate the source signals, deep clustering generates an embedding for each time-frequency bin, and separates sources by clustering the bins in the embedding space. We show that deep clustering outperforms conventional networks on a singing voice separation task, in both matched and mismatched conditions, even though conventional networks have the advantage of end-to-end training for best signal approximation, presumably because its more flexible objective engenders better regularization. Since the strengths of deep clustering and conventional network architectures appear complementary, we explore combining them in a single hybrid network trained via an approach akin to multi-task learning. Remarkably, the combination significantly outperforms either of its components.Comment: Published in ICASSP 201

Gunshot injuries of the spine

The Acute Spinal Injury Unit, relocated from Conradie Hospital to Groote Schuur Hospital in mid-2003, admitted 162 patients in the first year of its existence. A large number of these injuries were the result of interpersonal violence, particularly gunshot wounds. Aim. To review patients with gunshot injuries to the spine, with reference to neurological injury, associated injuries, need for surgery and complications. Methods. A comprehensive database is maintained to collect data on all spinal injury admissions. These data, as well as case notes and X-rays, were reviewed for all gunshot spine patients admitted to the Acute Spinal Injury Unit over a year. Forty-nine patients were identified. Thirty-eight were male and 11 female with an average age of 27.5 years (range 15 - 51 ± 8.53). The average stay in the acute unit was 30 (4 - 109 ± 28) days. Results. The spinal injury was complete in 38 and incomplete in 8, with 3 having no neurological deficit. The level was cervical in 13, thoracic in 24 and lumbar in 12. Only 9 patients improved neurologically. The spine was considered stable in 43 cases. Stabilisation was performed in the 6 unstable cases. The bullets were removed in 11 cases as they were in the canal. There were 55 significant associated injuries, viz. 14 haemo-pneumothoraces, 16 abdominal visceral injuries, 3 vascular injuries, 4 injuries of the brachial plexus and 3 of the oesophagus, 2 tracheal injuries, 1 soft palate injury and 11 non-spinal fractures. Complications included 3 deaths and discitis in 3 cases, pneumonia in 6 and pressure sores in 6. Conclusion. Gunshot injuries of the spine are a prevalent and resource-intensive cause of paralysis. There is a high incidence of permanent severe neurological deficit, but usually the spine remains mechanically stable. Most of the management revolves around the associated injuries and consequences of the neurological deficit