Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91

Scaling NVIDIA's Multi-speaker Multi-lingual TTS Systems with Zero-Shot TTS to Indic Languages

In this paper, we describe the TTS models developed by NVIDIA for the MMITS-VC (Multi-speaker, Multi-lingual Indic TTS with Voice Cloning) 2024 Challenge. In Tracks 1 and 2, we utilize RAD-MMM to perform few-shot TTS by training additionally on 5 minutes of target speaker data. In Track 3, we utilize P-Flow to perform zero-shot TTS by training on the challenge dataset as well as external datasets. We use HiFi-GAN vocoders for all submissions. RAD-MMM performs competitively on Tracks 1 and 2, while P-Flow ranks first on Track 3, with mean opinion score (MOS) 4.4 and speaker similarity score (SMOS) of 3.62.Comment: Presentation accepted at ICASSP 202

Experiments on the DCASE Challenge 2016: Acoustic scene classification and sound event detection in real life recording

International audienceIn this paper we present our work on Task 1 Acoustic Scene Classification and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.48 compared to the baseline of 0.91

PERT era, race‐based healthcare disparities in a large urban safety net hospital

Abstract Pulmonary embolism (PE) is the third leading cause of cardiovascular death in the United States. Black Americans have higher incidence, greater clot severity, and worse outcomes than White Americans. This disparity is not fully understood, especially in the context of the advent of PE response teams (PERT), which aim to standardize PE‐related care. This retrospective single‐center cohort study compared 294 Black and 131 White patients from our institution's PERT database. Primary objectives included severity and in‐hospital management. Secondary outcomes included length of stay, 30‐day readmission, 30‐day mortality, and outpatient follow‐up. Clot  (p = 0.42), acute treatment (p = 0.28), 30‐day mortality (p = 0.77), 30‐day readmission (p = 0.50), and outpatient follow‐up (p = 0.98) were similar between races. Black patients had a lower mean household income ($35,383, SD 20,596) than White patients ($63,396, SD 32,987) (p < 0.0001). More Black patients (78.8%) had exclusively government insurance (Medicare/Medicaid) compared to White patients (61.8%) (p = 0.006). Interestingly, government insurance patients had less follow‐up (58.3%) than private insurance patients (79.7%) (p = 0.001). Notably, patients with follow‐up had fewer 30‐day readmissions. Specifically, 12.2% of patients with follow‐up were readmitted compared to 22.2% of patients without follow‐up (p = 0.008). There were no significant differences in PE severity, in‐hospital treatment, mortality, or readmissions between Black and White patients. However, patients with government insurance had less follow‐up and more readmissions, indicating a socioeconomic disparity. Access barriers such as health literacy, treatment cost, and transportation may contribute to this inequity. Improving access to follow‐up care may reduce the disparity in PE outcomes

An approach for self-training audio event detectors using web data

Audio Event Detection (AED) aims to recognize sounds within audio and video recordings. AED employs machine learning algorithms commonly trained and tested on annotated datasets. However, available datasets are limited in number of samples and hence it is difficult to model acoustic diversity. Therefore, we propose combining labeled audio from a dataset and unlabeled audio from the web to improve the sound models. The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube. Whenever the detectors recognized any of the known sounds with high confidence, the unlabeled audio was use to re-train the detectors. The performance of the re-trained detectors is compared to the one from the original detectors using the annotated test set. Results showed an improvement of the AED, and uncovered challenges of using web audio from videos.Comment: 5 page

Prevalence and Clinical Characteristics of Dyssynergic Defecation and Slow Transit Constipation in Patients with Chronic Constipation

Patients with chronic constipation who do not respond to initial treatments often need further evaluation for dyssynergic defecation (DD) and slow transit constipation (STC). The aims of this study are to characterize the prevalence of DD and STC in patients referred to a motility center with chronic constipation and correlate diagnoses of DD and STC to patient demographics, medical history, and symptoms. High-resolution ARM (HR-ARM), balloon expulsion testing (BET) and whole gut transit scintigraphy (WGTS) of consecutive patients with chronic constipation were reviewed. Patients completed questionnaires describing their medical history and symptoms at the time of testing. A total of 230 patients completed HR-ARM, BET, and WGTS. Fifty (22%) patients had DD, and 127 (55%) patients had STC. Thirty patients (13%) had both DD and STC. There were no symptoms that were suggestive of STC vs. DD; however, patients with STC and DD reported more severe constipation than patients with normal transit and anorectal function. Patients with chronic constipation often need evaluation for both DD and STC to better understand their pathophysiology of symptoms and help direct treatment