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

Holographic Dark Energy Characterized by the Total Comoving Horizon and Insights to Cosmological Constant and Coincidence Problem

The observed acceleration of the present universe is shown to be well explained by the holographic dark energy characterized by the total comoving horizon of the universe ($\eta$HDE). It is of interest to notice that the very large primordial part of the comoving horizon generated by the inflation of early universe makes the $\eta$HDE behave like a cosmological constant. As a consequence, both the fine-tuning problem and the coincidence problem can reasonably be understood with the inflationary universe and holographical principle. We present a systematic analysis and obtain a consistent cosmological constraint on the $\eta$HDE model based on the recent cosmological observations. It is found that the $\eta$HDE model gives the best-fit result $\Omega_{m0}=0.270$ ($\Omega_{de0}=0.730$) and the minimal $\chi^2_{min}=542.915$ which is compatible with $\chi^2_{\Lambda {\rm CDM}}=542.919$ for the $\Lambda$CDM model.Comment: 17 pages, 4 figures, two eqs. (26)(27) added for the consistent approximate solution of dark energy in early universe, references added, published version in PR

Development of a Nongenetic Mouse Model of Type 2 Diabetes

Insulin resistance and loss of β-cell mass cause Type 2 diabetes (T2D). The objective of this study was to generate a nongenetic mouse model of T2D. Ninety-six 6-month-old C57BL/6N males were assigned to 1 of 12 groups including (1) low-fat diet (LFD; low-fat control; LFC), (2) LFD with 1 i.p. 40 mg/kg BW streptozotocin (STZ) injection, (3), (4), (5), (6) LFD with 2, 3, 4, or 5 STZ injections on consecutive days, respectively, (7) high-fat diet (HFD), (8) HFD with 1 STZ injection, (9), (10), (11), (12) HFD with 2, 3, 4, or 5 STZ injections on consecutive days, respectively. After 4 weeks, serum insulin levels were reduced in HFD mice administered at least 2 STZ injections as compared with HFC. Glucose tolerance was impaired in mice that consumed HFD and received 2, 3, or 4 injections of STZ. Insulin sensitivity in HFD mice was lower than that of LFD mice, regardless of STZ treatment. Islet mass was not affected by diet but was reduced by 50% in mice that received 3 STZ injections. The combination of HFD and three 40 mg/kg STZ injections induced a model with metabolic characteristics of T2D, including peripheral insulin resistance and reduced β-cell mass

Quantification of Flavin-containing Monooxygenases 1, 3, and 5 in Human Liver Microsomes by UPLC-MRM-Based Targeted Quantitative Proteomics and Its Application to the Study of Ontogeny

Flavin-containing monooxygenases (FMOs) have a significant role in the metabolism of small molecule pharmaceuticals. Among the five human FMOs, FMO1, FMO3, and FMO5 are the most relevant to hepatic drug metabolism. Although age-dependent hepatic protein expression, based on immunoquantification, has been reported previously for FMO1 and FMO3, there is very little information on hepatic FMO5 protein expression. To overcome the limitations of immunoquantification, an ultra-performance liquid chromatography (UPLC)-multiple reaction monitoring (MRM)-based targeted quantitative proteomic method was developed and optimized for the quantification of FMO1, FMO3, and FMO5 in human liver microsomes (HLM). A post-in silico product ion screening process was incorporated to verify LC-MRM detection of potential signature peptides before their synthesis. The developed method was validated by correlating marker substrate activity and protein expression in a panel of adult individual donor HLM (age 39–67 years). The mean (range) protein expression of FMO3 and FMO5 was 46 (26–65) pmol/mg HLM protein and 27 (11.5–49) pmol/mg HLM protein, respectively. To demonstrate quantification of FMO1, a panel of fetal individual donor HLM (gestational age 14–20 weeks) was analyzed. The mean (range) FMO1 protein expression was 7.0 (4.9–9.7) pmol/mg HLM protein. Furthermore, the ontogenetic protein expression of FMO5 was evaluated in fetal, pediatric, and adult HLM. The quantification of FMO proteins also was compared using two different calibration standards, recombinant proteins versus synthetic signature peptides, to assess the ratio between holoprotein versus total protein. In conclusion, a UPLC-MRM-based targeted quantitative proteomic method has been developed for the quantification of FMO enzymes in HLM

The upstream magnetic field of GRB shocks

Gamma-ray burst (GRB) afterglow emission is believed to be produced by synchrotron emission of electrons accelerated to high energy by a relativistic collisionless shock propagating into a weakly magnetized plasma. Afterglow observations have been used to constrain the post-shock magnetic field and structure, as well as the accelerated electron energy distribution. Here we show that X-ray afterglow observations on day time scale constrain the pre-shock magnetic field to satisfy B>0.2[n/(1/cc)]^{5/8} mG, where n is the pre-shock density. This suggests that either the shock propagates into a highly magnetized fast, v~10^3 km/s, wind, or that the pre-shock magnetic field is strongly amplified, most likely by the streaming of high energy shock accelerated particles. More stringent constraints may be obtained by afterglow observations at high photon energy at late, >1 d, times.Comment: Minor revisions, accepted to Ap

Application of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury

Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface