A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders

Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement. These studies developed iterative algorithms involving either Gibbs sampling or gradient descent at each step, making them computationally expensive. This paper proposes a variational inference method to iteratively estimate the power spectrogram of the clean speech. Our main contribution is the analytical derivation of the variational steps in which the en-coder of the pre-learned VAE can be used to estimate the varia-tional approximation of the true posterior distribution, using the very same assumption made to train VAEs. Experiments show that the proposed method produces results on par with the afore-mentioned iterative methods using sampling, while decreasing the computational cost by a factor 36 to reach a given performance .Comment: Submitted to INTERSPEECH 201

LibriMix: An Open-Source Dataset for Generalizable Speech Separation

In recent years, wsj0-2mix has become the reference dataset for single-channel speech separation. Most deep learning-based speech separation models today are benchmarked on it. However, recent studies have shown important performance drops when models trained on wsj0-2mix are evaluated on other, similar datasets. To address this generalization issue, we created LibriMix, an open-source alternative to wsj0-2mix, and to its noisy extension, WHAM!. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!. Using Conv-TasNet, we achieve competitive performance on all LibriMix versions. In order to fairly evaluate across datasets, we introduce a third test set based on VCTK for speech and WHAM! for noise. Our experiments show that the generalization error is smaller for models trained with LibriMix than with WHAM!, in both clean and noisy conditions. Aiming towards evaluation in more realistic, conversation-like scenarios, we also release a sparsely overlapping version of LibriMix's test set.Comment: submitted to INTERSPEECH 202

Filterbank design for end-to-end speech separation

International audienceSingle-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these fil-terbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2 ms windows

A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders : Supporting Document

Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement. These studies developed iterative algorithms involving either Gibbs sampling or gradient descent at each step, making them computationally expensive. This paper proposes a variational inference method to iteratively estimate the power spectrogram of the clean speech. Our main contribution is the analytical derivation of the variational steps in which the encoder of the pre-learned VAE can be used to estimate the variational approximation of the true posterior distribution, using the very same assumption made to train VAEs. Experiments show that the proposed method produces results on par with the aforementioned iterative methods using sampling, while decreasing the computational cost by a factor 36 to reach a given performance

Front Pharmacol

The COVID-19 epidemic has disrupted care and access to care in many ways. It was accompanied by an excess of cardiovascular drug treatment discontinuations. We sought to investigate a deeper potential impact of the COVID-19 epidemic on antihypertensive drug treatment disruptions by assessing whether the epidemic induced some changes in the characteristics of disruptions in terms of duration, treatment outcome, and patient characteristics. From March 2018 to February 2021, a repeated cohort analysis was performed using French national health insurance databases. The impact of the epidemic on treatment discontinuations and resumption of antihypertensive medications was assessed using preformed interrupted time series analyses either on a quarterly basis. Among all adult patients on antihypertensive medication, we identified 2,318,844 (18.7%) who discontinued their antihypertensive treatment during the first blocking period in France. No differences were observed between periods in the characteristics of patients who interrupted their treatment or in the duration of treatment disruptions. The COVID-19 epidemic was not accompanied by a change in the proportion of patients who fully resumed treatment after a disruption, neither in level nor in trend/slope [change in level: 2.66 (-0.11; 5.42); change in slope: -0.67 (-1.54; 0.20)]. Results were similar for the proportion of patients who permanently discontinued treatment within 1 year of disruption [level change: -0.21 (-2.08; 1.65); slope change: 0.24 (-0.40; 0.87)]. This study showed that, although it led to an increase in cardiovascular drug disruptions, the COVID-19 epidemic did not change the characteristics of these. First, disruptions were not prolonged, and post-disruption treatment outcomes remained unchanged. Second, patients who experienced antihypertensive drug disruptions during the COVID-19 outbreak were essentially similar to those who experienced disruptions before it

Phytalgic®, a food supplement, vs placebo in patients with osteoarthritis of the knee or hip: a randomised double-blind placebo-controlled clinical trial

International audienceINTRODUCTION: The medicinal treatment of osteoarthritis (OA) is mostly symptomatic to relieve pain and incapacity with analgesics and non-steroidal anti-inflammatory drugs (NSAIDs), drugs with well-known risks. Complementary medicines might reduce the symptoms of OA and decrease the need for NSAIDs. This study tested the effects of a food supplement, Phytalgic, on pain and function in patients with osteoarthritis and their use of analgesic and NSAIDs. METHODS: A randomized double-blind parallel-groups clinical trial compared Phytalgic (fish-oil, vitamin E, Urtica dioica) to a placebo for three months, in 81 patients with OA of the knee or hip using NSAIDs and/or analgesics regularly. The main outcome measures were use of NSAIDs (in Defined Daily Doses per day - DDD/day) or analgesics (in 500 mg paracetamol-equivalent tablets per week (PET/week) measured each month, and Western Ontario-McMaster University Osteo-Arthritis Index (WOMAC) function scales. RESULTS: After three months of treatment, the mean use of analgesics in the active arm (6.5 PET/week) vs. the placebo arm (16.5) was significantly different (P < 0.001) with a group mean difference of -10.0 (95% CI: -4.9 to -15.1). That of NSAIDs in the active arm (0.4 DDD/day) vs the placebo arm (1.0 DDD/day) was significantly different (P = 0.02) with a group mean difference of - 0.7 DDD/day (95% CI: -0.2 to -1.2). Mean WOMAC scores for pain, stiffness and function in the active arm (respectively 86.5, 41.4 and 301.6) vs the placebo arm (resp. 235.3, 96.3 and 746.5) were significantly different (P < 0.001) with group mean differences respectively of -148.8 (95% CI: -97.7 to -199.9), -54.9 (95% CI: -27.9 to -81.9) and -444.8 (95% CI: -269.1 to -620.4). CONCLUSIONS: The food supplement tested appeared to decrease the need for analgesics and NSAIDs and improve the symptoms of osteoarthritis. TRIAL REGISTRATION: Clinicaltrials.gov NCT00666523

Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

Chapter Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

Risk of Drug-Drug Interactions in Out-Hospital Drug Dispensings in France: Results From the DRUG-Drug Interaction Prevalence Study

Introduction: Drug interactions could account for 1% of hospitalizations in the general population and 2-5% of hospital admissions in the elderly. However, few data are available on the drugs concerned and the potential severity of the interactions encountered. We thus first aimed to estimate the prevalence of dispensings including drugs Contraindicated or Discommended because of Interactions (CDI codispensings) and to identify the most frequently involved drug pairs. Second, we aimed to investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved. Methods: We carried out a study using a random sample of all drugs dispensings registered in a database of the French Health Insurance System between 2010 and 2015. The distribution of the drugs involved was described considering active principles, detailing the 20 most frequent ones for both contraindicated or discommended codispensings (DCs). To investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved, we developed a specific indicator, the Drug-drug interaction prevalence study-score (DIPS-score), that compares for each drug pair the observed frequency of codispensing to its expected probability. The latter is determined considering the frequencies of dispensings of the individual drugs constituting a pair of interest. Results: We analyzed 6,908,910 dispensings: 13,196 (0.2%) involved contraindicated codispensings (CCs), and 95,410 (1.4%) DCs. For CCS, the most frequently involved drug pair was "bisoprolol+flecainide" = 5,036); four out of five of the most represented pairs involved cardiovascular drugs. For DCS, the most frequently involved drug pair was "ramipril+spironolactone" = 4,741); all of the five most represented pairs involved cardiovascular drugs. The drug pair involved in the CC with the highest score value was "citalopram+hydroxyzine" (DIPS-score: 3.7; 2.9-4.6); that with the lowest score was "clarithromycin+simvastatin" (DIPS-score: 0.2; 0.2-0.3). DIPS-score median value was 0.4 for CCs and 0.6 for DCs. Conclusion: This high prevalence of CDI codispensings enforces the need for further risk-prevention actions regarding drug-drug interactions (DDIs), especially for arrhythmogenic or anti-arrhythmic drugs. In this perspective, the DIPS-score we develop could ease identifying the interactions that are poorly considered by clinicians/pharmacists and targeting interventions