CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis

BACKGROUND: Sleep difficulties might be a contributory causal factor in the occurrence of mental health problems. If this is true, improving sleep should benefit psychological health. We aimed to determine whether treating insomnia leads to a reduction in paranoia and hallucinations. METHODS: We did this single-blind, randomised controlled trial (OASIS) at 26 UK universities. University students with insomnia were randomly assigned (1:1) with simple randomisation to receive digital cognitive behavioural therapy (CBT) for insomnia or usual care, and the research team were masked to the treatment. Online assessments took place at weeks 0, 3, 10 (end of therapy), and 22. The primary outcome measures were for insomnia, paranoia, and hallucinatory experiences. We did intention-to-treat analyses. The trial is registered with the ISRCTN registry, number ISRCTN61272251. FINDINGS: Between March 5, 2015, and Feb 17, 2016, we randomly assigned 3755 participants to receive digital CBT for insomnia (n=1891) or usual practice (n=1864). Compared with usual practice, the sleep intervention at 10 weeks reduced insomnia (adjusted difference 4·78, 95% CI 4·29 to 5·26, Cohen's d=1·11; p<0·0001), paranoia (-2·22, -2·98 to -1·45, Cohen's d=0·19; p<0·0001), and hallucinations (-1·58, -1·98 to -1·18, Cohen's d=0·24; p<0·0001). Insomnia was a mediator of change in paranoia and hallucinations. No adverse events were reported. INTERPRETATION: To our knowledge, this is the largest randomised controlled trial of a psychological intervention for a mental health problem. It provides strong evidence that insomnia is a causal factor in the occurrence of psychotic experiences and other mental health problems. Whether the results generalise beyond a student population requires testing. The treatment of disrupted sleep might require a higher priority in mental health provision. FUNDING: Wellcome Trust

Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries

BackgroundThe COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that at its most extreme, this may manifest itself in increased suicide rates.MethodsWe sourced real-time suicide data from around the world via a systematic internet search and recourse to our networks and the published literature. We used interrupted time series analysis to model the trend in monthly suicides prior to COVID-19 in each country/area-within-country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic. Countries/areas-within countries contributed data from at least 1 January 2019 to 31 July 2020 and potentially from as far back as 1 January 2016 until as recently as 31 October 2020. We conducted a primary analysis in which we treated 1 April to 31 July 2020 as the COVID-19 period, and two sensitivity analyses in which we varied its start and end dates (for those countries/areas-within-countries with data beyond July 2020).OutcomesWe sourced data from 21 countries (high income [n=16], upper-middle income [n=5]; whole country [n=10], area(s)-within-the-country [n=11]). In general, there does not appear to have been a significant increase in suicides since the pandemic began in the countries for which we had data. In fact, in a number of countries/areas-within-countries there appears to have been a decrease.InterpretationThis is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. It offers a consistent picture, albeit from high- and upper-middle income countries, of suicide numbers largely remaining unchanged or declining in the early months of the pandemic. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic impacts of the pandemic unfold

Content-Based Music Recommendation with the LFM-1b Dataset and Sample-Level Deep Convolutional Neural Networks

Content-based music classification systems attempt to predict musical attributes of songs directly from their audio content. Commonly, the ground truth labels are explicitly identified traits such as the genre of a piece of music. Ground truth annotations can also be derived implicitly from user listening patterns, leading to content-based music recommendation systems.We improve upon previous work in content-based music recommendation in two ways. One, we match the Million Song Dataset (MSD) to the recent LFM-1b dataset, which is much larger than the standard Taste Profile Subset. Two, we train our model using deep convolutional architectures to predict latent factors directly from the audio of the music, instead of the standard practice of training on an intermediate time-frequency representation. We also evaluate the effectiveness of using latent factor prediction as a source task for tag prediction via transfer learning

Content-Based Music Recommendation with the LFM-1b Dataset and Sample-Level Deep Convolutional Neural Networks

Content-based music classification systems attempt to predict musical attributes of songs directly from their audio content. Commonly, the ground truth labels are explicitly identified traits such as the genre of a piece of music. Ground truth annotations can also be derived implicitly from user listening patterns, leading to content-based music recommendation systems.We improve upon previous work in content-based music recommendation in two ways. One, we match the Million Song Dataset (MSD) to the recent LFM-1b dataset, which is much larger than the standard Taste Profile Subset. Two, we train our model using deep convolutional architectures to predict latent factors directly from the audio of the music, instead of the standard practice of training on an intermediate time-frequency representation. We also evaluate the effectiveness of using latent factor prediction as a source task for tag prediction via transfer learning

Incretin based drugs and risk of cholangiocarcinoma among patients with type 2 diabetes: population based cohort study

International audienceOBJECTIVE: To determine whether use of dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists are associated with an increased risk of cholangiocarcinoma in adults with type 2 diabetes.DESIGN: Population based cohort study.SETTING: General practices contributing data to the UK Clinical Practice Research Datalink.PARTICIPANTS: 154 162 adults newly treated with antidiabetic drugs between 1 January 2007 and 31 March 2017, followed until 31 March 2018.MAIN OUTCOME MEASURES: Use of DPP-4 inhibitors and GLP-1 receptor agonists was modelled as a time varying variable and compared with use of other second or third line antidiabetic drugs. All exposures were lagged by one year to account for cancer latency and to minimise reverse causality. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals of incident cholangiocarcinoma associated with use of DPP-4 inhibitors and GLP-1 receptor agonists, separately. A post hoc pharmacovigilance analysis was conducted using the World Health Organization's global individual case safety report database, VigiBase, to estimate reporting odds ratios of cholangiocarcinoma.RESULTS: During 614 274 person years of follow-up, 105 incident cholangiocarcinoma events occurred (rate 17.1 per 100 000 person years). Use of DPP-4 inhibitors was associated with a 77% increased hazard of cholangiocarcinoma (hazard ratio 1.77, 95% confidence interval 1.04 to 3.01). Use of GLP-1 receptor agonists was associated with an increased hazard with a wide confidence interval (hazard ratio 1.97, 0.83 to 4.66). In the pharmacovigilance analysis, the use of DPP-4 inhibitors and GLP-1 receptor agonists were both associated with increased reporting odds ratios for cholangiocarcinoma, compared with use of sulfonylureas or thiazolidinediones (1.63, 1.00 to 2.66, 4.73, 2.95 to 7.58, respectively).CONCLUSION: Compared with use of other second or third line antidiabetic drugs, use of DPP-4 inhibitors, and possibly GLP-1 receptor agonists, might be associated with an increased risk of cholangiocarcinoma in adults with type 2 diabetes

Sodium-Glucose Co-Transporter 2 inhibitors and the Short-term Risk of Bladder Cancer: An International Multi-Site Cohort Study

   Objective: To determine whether sodium-glucose co-transporter 2 (SGLT2) inhibitors, compared with glucagon-like peptide-receptor agonists (GLP-1RAs) or dipeptidyl peptidase-4 (DPP4) inhibitors are associated with an increased risk of early bladder cancer events. Research Design & Methods: We conducted a multi-site, population-based, new-user active comparator cohort study using the United Kingdom Clinical Practice Research Datalink, Medicare fee-for-service, Optum© Clinformatics® Data Mart, and MarketScan Health from January 2013 through December 2020. We assembled two cohorts of adults with type 2 diabetes initiating (1) SGLT2 inhibitors or GLP-1RAs, and (2) SGLT2 inhibitors or DPP4 inhibitors. Cox proportional hazards models were fit to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of incident bladder cancer. The models were weighted using propensity score fine stratification. Site-specific HRs were pooled using random-effects models. Results: SGLT2 inhibitor (n=453,560) and GLP-1RA (n=375,997) users had a median follow-up ranging from 1.5-2.2 years. Overall, SGLT2 inhibitors were not associated with an increased risk of bladder cancer, compared with GLP-1RAs (HR=0.90; 95% CI: 0.81-1.00). Similarly, when compared with DPP-4 inhibitors (n=853,186), SGLT2 inhibitors (n=347,059) were not associated with an increased risk of bladder cancer (HR: 0.99, 95% CI: 0.91-1.09) over a median follow-up ranging from 1.6-2.6 years. Results were consistent across sensitivity analyses. Conclusions: Contrary to previous randomized controlled trials, these findings indicate that the use of SGLT2 inhibitors is not associated with an increased risk of bladder cancer when compared with GLP-1RAs or DPP4 inhibitors. This should provide reassurance on the short-term effects of SGLT2 inhibitors on bladder cancer incidence.</p